基于不完全信息的生物网络随机非线性建模与控制研究
|
摘要
在生物网络以及大型复杂工业系统中存在着大量的随机不确定性现象,例如基因调控网络中单个基因地产生、消亡,神经网络中介质传递过程以及周围环境中扰动地影响,都会引发网络的随机性和不确定性。对于这样的系统,受到物理条件以及知识地限制,其机理模型一般难以用准确的数学模型来描述,表现出强烈的随机特性。非线性在生物网络以及各类工业系统系统中普遍存在,所以针对随机非线性系统这类综合性问题地研究具有重大的应用价值,同时也是研究控制系统的难点所在。现代控制理论以状态空间为主,依赖系统精确的数学模型。但是由于被控系统复杂、受到各种随机扰动地影响,系统中的关键状态无法通过测量系统输出直接得到。因此需要设计合适的状态估计器对被控对象的状态进行估计,间接地获取被控系统状态,为后续地分析、设计控制系统作铺垫,例如利用估计状态设计状态反馈控制器等。系统测量输出的过程中不可避免地会发生一些无法预知的变化,例如测量数据延迟、信号量化、信号采样等。在这些不完全信息地影响下,控制系统的性能变差,甚至产生不稳定、震荡现象。考虑不完全信息影响下的随机非线性系统,设计合适的状态估计器以及控制器,使得控制系统取得优越的性能。
因此,本论文以随机Lyapunov稳定性理论为基础、线性矩阵不等式为工具,研究了不完全信息影响下生物网络随机非线性建模与控制问题,提出了新的思路,给出了新的结果,主要工作如下：
(1)针对测量数据概率延迟下的离散随机基因调控网络设计了鲁棒H∞状态估计器,其模型中考虑了参数的范数有界不确定性、随机扰动以及时变时滞的影响。测量延迟现象用条件概率分布的二进制开关序列来描述,激励函数满足扇形边界条件。通过Lyapunov稳定性理论和随机分析技术,得到了均方意义下的鲁棒指数稳定估计器以及在给定的H∞扰动衰减水平下随机鲁棒稳定估计器存在的充分条件,通过求解一组线性矩阵不等式得到。最后,通过一个数值例子来说明所提出方法的有效性。
(2)考虑了随机中立型神经网络状态估计问题。常量时滞以及分布时滞影响下,估计误差系统均方指数稳定以及几乎必然指数稳定。激励函数以及非线性测量函数均满足Lipschitz条件,通过构造合适的Lyapunov-Krasovskii泛函得到了保证估计器存在的时滞相关判据,为一线性矩阵不等式的解。最后通过两个数值例子说明了理论结果的合理性,以及能够提供保守性更小的解。
(3)针对离散通信以及节点时滞影响下的连续生物动态网络设计了自适应反馈同步控制器。网络中节点特性连续,节点之间只在离散瞬间进行通信,即通过离散通信方式交换信息。通信周期内的通信时间可以是变化不确定的。通过构造分段连续的Lyapunov-Krasovskii泛函来约束离散通信方式的特性,得到了自适应反馈控制器存在性的判据。在自适应更新律以及控制器的作用下,网络能够达到全局指数稳定同步。最后,通过两种不同耦合类型的生物动态网络,全局耦合动态网络以及最近邻连接动态网络来说明了自适应反馈同步控制器的有效性。
(4)多级加工系统由单级系统串联而成,当扰动影响某级系统的生产时,多级系统最终的产品质量也会下降。本论文设计了基于基因调控网络的多级牵伸环节生物启发协同控制器,在扰动地影响下,维持产品性能指标稳定在期望值。对生物启发协同控制器的稳定性、收敛性进行了理论证明,利用多目标优化算法优化控制器参数,使得性能指标和期望值之间的误差以及调整时间最小,最后将生物启发协同控制器成功应用在了化纤生产的多级牵伸过程中。
最后,对论文的研究内容进行了总结,讨论了将来需要进一步研究的方向。
The stochastic phenomena and uncertainty are quite commen in the biological networks and the industrial plants. Gene regulation is an intrinsically noisy process due to intracellular and extracellular noise perturbations, which are derived from random births and deaths of individual molecules and environmental fluctuations. In the neural systems, the synaptic transmission is a noisy process brought on by random fluctuation from the release of neuron transmitters and other probabilistic causes in real nervous systems. Subject to physical conditions and restrictions on the knowledge, it is imposible to obtain the deterministic model to describe the systems' dynamic characteristics, which show random characterics. The nonlinear systems are commonly encountered in the engineering fields and the nature. Therefore, it is of a great importance to investigate the stochastic nonlinear systems in theoretical and application, which is hard to be controlled. Based on the state space, the modern control theory utilizes the accurete mathematical model to design the controller. While, for the complexity of large-scale networks and the effects of disturbances, the states of the network are hard or even impossible to be obtained directly or completely. So, in order to make full use of the states, one may need to estimate the states through available outputs, and then utilize the estimated state to achieve certain objective, such as state feedback control. There are some inevitably and unpredictable changes in the measurement of the output, for example probabilistic measurement delays, signal sampling, and quantization effects, which are named as imcomplete information. The imcomplete information may induce instability, oscillations or poor performances, which should be taken into account in the control systems.
In this thesis, we disuss the state estimation and control iusse for several typical stochastic nonlinear systems, genetic regulation systems, neural systems, and complex dynamical systems with the impletement information. Based on Lyapunov-Krasovskii functional mtheod, linear matrix inequalities, and so on, sufficient conditions are established to ensure the existence of the desired estimators and the controllers. The main contributions and the main contents are as follows:
(1) The robust H∞state estimation problem is investigated for a class of discrete-time stochastic genetic regulatory networks (GRNs) with probabilistic measurement delays. Norm-bounded uncertainties, stochastic disturbances and time-varying delays are considered in the discrete-time stochastic GRNs. Meantime the measurement delays of GRNs are described by a binary switching sequence satisfying a conditional probability distribution. The main purpose is to design a linear estimator to approximate the true concentrations of the mRNA and the protein through the available measurement outputs. Based on Lyapunov stability theory and stochastic analysis techniques, sufficient conditions are first established to ensure the existence of the desired estimators in the terms of a linear matrix inequality (LMI). Then, the explicit expression of the desired estimator is shown to ensure the estimation error dynamics to be robustly exponentially stable in the mean square and a prescribed H∞disturbance rejection attenuation is guaranteed for the addressed system. Finally, a numerical example is presented to show the effectiveness of the proposed results.
(2) The state estimation for stochastic neural networks of neutral type with discrete and distributed delays is considered. By using available output measurements, the state estimator can approximate the neuron states, and the asymptotic property of the state error is mean square exponential stable and also almost surely exponential stable in the presence of discrete and distributed delays. Under the Lipschitz assumptions for the activation functions and the measurement nonlinearity, a delay-dependent linear matrix inequality (LMI) criterion is proposed to guarantee the existence of the desired estimators by constructing an appropriate Lyapunov-Krasovskii function. It is shown that the existence conditions and the explicit expression of the state estimator can be parameterized in terms of the solution to a LMI. Finally, two numerical examples are presented to demonstrate the validity of the theoretical results and show that the theorem can provide less conservative conditions.
(3) The synchronization of continuous complex dynamical networks with discrete-time communications and delayed nodes is investigated. The nodes in the dynamical networks act in the continuous manner. While the communications between nodes are discrete-time, that is, they communicate with others only at discrete time instants. The communication intervals in communication period can be uncertain and variable. By using a piecewise Lyapunov-Krasovskii function to govern the characteristics of the discrete communications instants, we investigate the adaptive feedback synchronization and a criterion is derived to guarantee the existence of the desired controllers. The globally exponentially synchronization can be achieved by the controllers under the updating laws. Finally, two numerical examples including globally coupled network and nearest-neighbour coupled networks are presented to demonstrate the validity and effectiveness of the proposed control scheme.
(4) The multi-stage system in manufacture has a series structure of a single-stage production system. Once the vibrations in one stage affect the quality of its product, the performance of the finished products in the multi-stage system is degraded, too. A bio-inspired cooperative controller via evolution of gene regulatory network is presented to make the performance index of each stage stable over the whole study horizon and the overall performance index close to the desired value against vibrations simultaneously. A theoretical proof of the controller convergence to the desired overall performance index is also provided. This developmental controller is evolved using a multi-objective optimization algorithm subject to minimize the absolute value of the error between the desired overall performance index and the actual one and shorten the settling time. By using the bio-inspired cooperative controller, there is a decrease in scrap and eventually an improvement of the product quality. Furthermore, the implementation of the proposed bio-inspired cooperative controller is illustrated and examined through the multi-stage drafting system from the chemical fiber process industry. The experimental results demonstrate that the proposed controller should have wide application in similar multi-stage complex systems.
At the end, we summarize the reselt of the thesis, and present some future works which required further investigation.
因此,本论文以随机Lyapunov稳定性理论为基础、线性矩阵不等式为工具,研究了不完全信息影响下生物网络随机非线性建模与控制问题,提出了新的思路,给出了新的结果,主要工作如下：
(1)针对测量数据概率延迟下的离散随机基因调控网络设计了鲁棒H∞状态估计器,其模型中考虑了参数的范数有界不确定性、随机扰动以及时变时滞的影响。测量延迟现象用条件概率分布的二进制开关序列来描述,激励函数满足扇形边界条件。通过Lyapunov稳定性理论和随机分析技术,得到了均方意义下的鲁棒指数稳定估计器以及在给定的H∞扰动衰减水平下随机鲁棒稳定估计器存在的充分条件,通过求解一组线性矩阵不等式得到。最后,通过一个数值例子来说明所提出方法的有效性。
(2)考虑了随机中立型神经网络状态估计问题。常量时滞以及分布时滞影响下,估计误差系统均方指数稳定以及几乎必然指数稳定。激励函数以及非线性测量函数均满足Lipschitz条件,通过构造合适的Lyapunov-Krasovskii泛函得到了保证估计器存在的时滞相关判据,为一线性矩阵不等式的解。最后通过两个数值例子说明了理论结果的合理性,以及能够提供保守性更小的解。
(3)针对离散通信以及节点时滞影响下的连续生物动态网络设计了自适应反馈同步控制器。网络中节点特性连续,节点之间只在离散瞬间进行通信,即通过离散通信方式交换信息。通信周期内的通信时间可以是变化不确定的。通过构造分段连续的Lyapunov-Krasovskii泛函来约束离散通信方式的特性,得到了自适应反馈控制器存在性的判据。在自适应更新律以及控制器的作用下,网络能够达到全局指数稳定同步。最后,通过两种不同耦合类型的生物动态网络,全局耦合动态网络以及最近邻连接动态网络来说明了自适应反馈同步控制器的有效性。
(4)多级加工系统由单级系统串联而成,当扰动影响某级系统的生产时,多级系统最终的产品质量也会下降。本论文设计了基于基因调控网络的多级牵伸环节生物启发协同控制器,在扰动地影响下,维持产品性能指标稳定在期望值。对生物启发协同控制器的稳定性、收敛性进行了理论证明,利用多目标优化算法优化控制器参数,使得性能指标和期望值之间的误差以及调整时间最小,最后将生物启发协同控制器成功应用在了化纤生产的多级牵伸过程中。
最后,对论文的研究内容进行了总结,讨论了将来需要进一步研究的方向。
The stochastic phenomena and uncertainty are quite commen in the biological networks and the industrial plants. Gene regulation is an intrinsically noisy process due to intracellular and extracellular noise perturbations, which are derived from random births and deaths of individual molecules and environmental fluctuations. In the neural systems, the synaptic transmission is a noisy process brought on by random fluctuation from the release of neuron transmitters and other probabilistic causes in real nervous systems. Subject to physical conditions and restrictions on the knowledge, it is imposible to obtain the deterministic model to describe the systems' dynamic characteristics, which show random characterics. The nonlinear systems are commonly encountered in the engineering fields and the nature. Therefore, it is of a great importance to investigate the stochastic nonlinear systems in theoretical and application, which is hard to be controlled. Based on the state space, the modern control theory utilizes the accurete mathematical model to design the controller. While, for the complexity of large-scale networks and the effects of disturbances, the states of the network are hard or even impossible to be obtained directly or completely. So, in order to make full use of the states, one may need to estimate the states through available outputs, and then utilize the estimated state to achieve certain objective, such as state feedback control. There are some inevitably and unpredictable changes in the measurement of the output, for example probabilistic measurement delays, signal sampling, and quantization effects, which are named as imcomplete information. The imcomplete information may induce instability, oscillations or poor performances, which should be taken into account in the control systems.
In this thesis, we disuss the state estimation and control iusse for several typical stochastic nonlinear systems, genetic regulation systems, neural systems, and complex dynamical systems with the impletement information. Based on Lyapunov-Krasovskii functional mtheod, linear matrix inequalities, and so on, sufficient conditions are established to ensure the existence of the desired estimators and the controllers. The main contributions and the main contents are as follows:
(1) The robust H∞state estimation problem is investigated for a class of discrete-time stochastic genetic regulatory networks (GRNs) with probabilistic measurement delays. Norm-bounded uncertainties, stochastic disturbances and time-varying delays are considered in the discrete-time stochastic GRNs. Meantime the measurement delays of GRNs are described by a binary switching sequence satisfying a conditional probability distribution. The main purpose is to design a linear estimator to approximate the true concentrations of the mRNA and the protein through the available measurement outputs. Based on Lyapunov stability theory and stochastic analysis techniques, sufficient conditions are first established to ensure the existence of the desired estimators in the terms of a linear matrix inequality (LMI). Then, the explicit expression of the desired estimator is shown to ensure the estimation error dynamics to be robustly exponentially stable in the mean square and a prescribed H∞disturbance rejection attenuation is guaranteed for the addressed system. Finally, a numerical example is presented to show the effectiveness of the proposed results.
(2) The state estimation for stochastic neural networks of neutral type with discrete and distributed delays is considered. By using available output measurements, the state estimator can approximate the neuron states, and the asymptotic property of the state error is mean square exponential stable and also almost surely exponential stable in the presence of discrete and distributed delays. Under the Lipschitz assumptions for the activation functions and the measurement nonlinearity, a delay-dependent linear matrix inequality (LMI) criterion is proposed to guarantee the existence of the desired estimators by constructing an appropriate Lyapunov-Krasovskii function. It is shown that the existence conditions and the explicit expression of the state estimator can be parameterized in terms of the solution to a LMI. Finally, two numerical examples are presented to demonstrate the validity of the theoretical results and show that the theorem can provide less conservative conditions.
(3) The synchronization of continuous complex dynamical networks with discrete-time communications and delayed nodes is investigated. The nodes in the dynamical networks act in the continuous manner. While the communications between nodes are discrete-time, that is, they communicate with others only at discrete time instants. The communication intervals in communication period can be uncertain and variable. By using a piecewise Lyapunov-Krasovskii function to govern the characteristics of the discrete communications instants, we investigate the adaptive feedback synchronization and a criterion is derived to guarantee the existence of the desired controllers. The globally exponentially synchronization can be achieved by the controllers under the updating laws. Finally, two numerical examples including globally coupled network and nearest-neighbour coupled networks are presented to demonstrate the validity and effectiveness of the proposed control scheme.
(4) The multi-stage system in manufacture has a series structure of a single-stage production system. Once the vibrations in one stage affect the quality of its product, the performance of the finished products in the multi-stage system is degraded, too. A bio-inspired cooperative controller via evolution of gene regulatory network is presented to make the performance index of each stage stable over the whole study horizon and the overall performance index close to the desired value against vibrations simultaneously. A theoretical proof of the controller convergence to the desired overall performance index is also provided. This developmental controller is evolved using a multi-objective optimization algorithm subject to minimize the absolute value of the error between the desired overall performance index and the actual one and shorten the settling time. By using the bio-inspired cooperative controller, there is a decrease in scrap and eventually an improvement of the product quality. Furthermore, the implementation of the proposed bio-inspired cooperative controller is illustrated and examined through the multi-stage drafting system from the chemical fiber process industry. The experimental results demonstrate that the proposed controller should have wide application in similar multi-stage complex systems.
At the end, we summarize the reselt of the thesis, and present some future works which required further investigation.
引文
[1]B. Oksendal, Stochastic differential equation-an introduction with application, sixth editon [M], New York:Springer,2010.
[2]方洋旺,潘进,随机系统分析与应用[M],西安：西北工业大学出版社,2006.
[3]方洋旺,随机系统最优控制[M],北京：清华大学出版社,2005.
[4]X.-R. Mao, Exponential stability of stochastic differential equations [M], NewYork:Marcel Dekker,1994.
[5]X.-R. Mao, Stochastic differential equations and applications,2nd Edition [M], Horwood,2008.
[6]A. Friedman, Stochastic differential equations and applications [M], Academic Press, Vol.1,1975, VO1.2,1983.
[7]龚光鲁,随机微分方程引论[M],北京：北京大学出版社,1987.
[8]李树英,许茂增,随机系统的滤波与控制[M],北京：国防工业出版社,1991.
[9]胡适耕,随机微分方程[M],北京：科学出版社,2008.
[10]杨玲玲,章云,陈贞丰,不确定非线性系统基于偏差分离的双线性控制,自动化学报,2010,36(10)：1432-1442.
[11]宋立忠,鄢圣茂,杨立秋,不确定系统鲁棒自适应离散变结构控制[J],自动化学报,2011,37(8)：1024-1028.
[12]胡庆雷,马广富,姜野,刘亚秋,三轴稳定挠性卫星姿态机动时变滑模变结构和主动振动控制[J],控制理论与应用,2009,26(2)：122-126.
[13]Salah Bouhouche, Malek Lahreche, Jurgen Bast, Control of heat transfer in continous casting process using neural networks [J], Acta Autom. Sinica,2008, 34(6):701-706.
[14]王良勇,柴天佑,方正,考虑驱动系统动态的机械手神经网络控制及应用[J],自动化学报,2009,35(5)：622-626.
[15]陈刚,柴毅,丁宝苍,魏善碧,电液位置伺服系统的多滑模神经网络控制[J],控制与决策,2009,24(2)：221-225.
[16]朱明超,李英,李元春,姜日花,基于观测器的可重构机械臂分散自适应模糊控制[J],控制与决策,2009,24(3)：429-434.
[17]孙云平,李俊民,李靖,一类非线性参数化系统自适应重复学习控制[J],控 制理论与应用,2009,26(2)：228-232.
[18]贾涛,刘军,钱富才,一类非线性时滞系统的自适应模糊动态面控制[J],自动化学报,2011,37(1)：83-91.
[19]丁海山,毛剑琴,林岩,基于模糊树模型的间接自适应模糊控制[J],自动化学报,2008,34(6)：678-683.
[20]胡志坤,孙岩,姜斌,何静,张昌凡,一种基于最优未知输入观测器的故障诊断方法[J],自动化学报,2013,39(1)：1-6.
[21]王永富,王殿辉,柴天佑,基于状态估计的摩擦模糊建模与鲁棒自适应控制[J],自动化学报,2011,37(2)：245-251.
[22]周聪,肖建,改进强跟踪滤波算法及其在汽车状态估计中的应用[J],自动化学报,2012,38(9)：1520-1527.
[23]文成林,周东华,多尺度估计理论及其应用[M].北京：清华大学出版社,2002.
[24]Mehra R K. On the identification of variances and adaptive Kalman filtering[J]. IEEE Trans. Autom. Control,1970,AC5(2):175-184.
[25]邓自立,李北新,自适应Kalman滤波器及其应用[J],自动化学报,1992,18(4)：408-413.
[26]高媛,邓自立,带相关观测噪声系统的自校正观测融合Kalman滤波器[J],科学技术与工程,2009,9(7)：1669-1677.
[27]揭慧,张明波,邓自立,多传感器ARMA信号自校正分布式融合Kalman滤波器[J],科学技术与工程,2011,11(3)：443-447,460.
[28]梁化勇,周彤,一类空间连接系统的分布式状态估计及其收敛性分析[J],自动化学报,2010,36(5)：720-730.
[29]Petersen I R, Savkin A V, Robust Kalman Filtering for Signal and System with Large Uncertainties [M]. Boston:Birkhauser Boston,1999.
[30]Fuwen Yang, Zidong Wang, and Y. S. Hung, Robust kalman filtering for discrete yime-varying uncertain systems with multiplicative noises [J], IEEE Trans. Autom. Control,2002,47(7):1179-1183.
[31]Zidong Wang, Daniel W. C. Ho, and Xiaohui Liu, State estimation for delayed neural networks [J]. IEEE Trans. Neural Networks,2005,16(1):279-284.
[32]Zidong Wang, H. Unbehauen, Robust H2/H∞-state estimation for systems with wrror variance constraints:the continuous-time case [J], IEEE Trans. Autom. Control,1999,44(5):1061-1065.
[33]Jinling Liang, James Lamb, Zidong Wang, State estimation for Markov-type genetic regulatory networks with delays and uncertain mode transition rates [J]. Phys. Lett. A,2009 (373):4328-4337.
[34]Zidong Wang, Yurong Liu, Xiaohui Liu, State estimation for jumping recurrent neural networks with discrete and distributed delays [J], Neural Networks, 2009(22):41-48.
[35]Jinling Liang, Zidong Wang, Xiaohui Liu. State estimation for coupled uncertain stochastic networks with missing measurements and time-varying delays:the discrete-time case [J]. IEEE Trans. Neural Networks,2009,20(5): 781-793.
[36]Bo Shen, Zidong Wang, Xiaohui Liu, Bounded H∞ synchronization and state estimation for discrete time-varying stochastic complex networks over a finite horizon [J], IEEE Trans. Neural Networks,2011,22(1):145-157.
[37]Jinling Liang, Zidong Wang, Xiaohui Liu, Distributed state estimation for discrete-time sensor networks with randomly varying nonlinearities and missing measurements [J]. IEEE Trans. Neural Networks,2011,22(3):486-496.
[38]Guoliang Wei, Zidong Wang, Huisheng Shu, Robust filtering with stochastic nonlinearities and multiple missing measurements [J], Automatica,2009,45: 836-841.
[39]Lifeng Ma, Zidong Wang, Jun Hu, Yuming Bo, Zhi Guo, Robust variance-constrained filtering for a class of nonlinear stochastic systems with missing measurements [J], Signal Process.,2010,90:2060-2071.
[40]Deok-Jin Lee, Nonlinear estimation and multiple sensor fusion using unscented information filtering [J]. IEEE Signal Process. Lett.,2008,15:861-864.
[41]Andrea Benigni, Junqi Liu, Ferdinanda Ponci, Antonello Monti, Giuditta Pisano, Sara Sulis, Decoupling power system state estimation by means of stochastic collocation [J]. IEEE Trans. Instrum. Meas.,2011,60(5):1623-1632.
[42]Amin Zia, Thia Kirubarajan, James P. Reilly, Derek Yee, Kumaradevan Punithakumar, Shahram Shirani. An EM algorithm for nonlinear state estimation with model uncertainties [J], IEEE Trans. Instrum. Meas.,2008, 56(3):921-936.
[43]H. K. Khalil, Nonlinear systems, third edtion [M], Englewood Cliffs, NJ: Prentice-Hall,2002.
[44]平续斌,丁宝苍,动态输出反馈鲁棒模型预测控制离线算法[J],自动化学报,2013,39(6)：790-798.
[45]Jie Tian, Xue-Jun Xie, Adaptive state-feedback stabilization for more general high-order stochastic nonlinear systems [J], Acta Automatica Sinica,34(9): 1188-1191.
[46]年晓红,曹莉,基于微分对策的最优状态观测器和最优状态反馈控制器的设计[J],自动化学报,2006,32(5)：807-812.
[47]陈东彦,司玉琴,具有饱和状态反馈离散时滞系统的渐进稳定性[J],自动化学报,2008,34(11)：1445-1448.
[48]陶洪峰,胡寿松,蔡俊伟,变时滞T-S模糊系统的L∞静态输出反馈控制[J],自动化学报,2008,34(4)：453-459.
[49]刘月,马树萍,时滞系统的输出反馈滑模控制的一种奇异系统方法[J],自动化学报,2013,39(5)：594-601.
[50]刘磊明,童朝南,武延坤,一种带有动态输出反馈控制器的网络控制系统的Markov跳变模型[J],自动化学报,2009,35(5)：627-631.
[51]李靖,李俊民,陈为胜,随机非线性严格反馈系统的自适应神经网络输出反馈镇定[J],自动化学报,2010,36(3)：450-453.
[52]张利军,杨立新,郭立东,孙立宁,压电陶瓷驱动平台自适应输出反馈控制[J],自动化学报,2012,38(9)：1550-1556.
[53]X. R. Han, Emilia Fridman, Sarah K. Spurgeon, and Chris Edwards, On the design of sliding-mode static-output-feedback controllers for systems with state delay [J], IEEE Trans. Ind Electron.,2009,56(9):3656-3664.
[54]R.E. Mirollo, S.H. Strogatz, Synchronization of pulse-coupled biological oscillators [J], SIAM J. Appl. Math.,1990,50(6):1645-1662.
[55]Z. Li, Z. Duan, G. Chen, and L. Huang, Consensus of multiagent systems and synchronization of complex networks:a unified viewpoint [J], IEEE Trans. Circuits Syst. I, Reg. Papers,2000,57:213-224.
[56]J. Yi, Y. Wang, J. Xiao, Y. Huang, Exponential synchronization of complex dynamical networks with markovian jump parameters and stochastic delays and its application to multi-agent systems [J], Commun Nonlinear Sci Numer Simulat,20tt,18:1175-1192.
[57]Z. Meng, Z. Li, A. V. Vasilakos, and S. Chen, Delay-induced synchronization of identical linear multiagent mystems [J], IEEE Trans. Cybern.,2013,43: 476-489.
[58]Y. Tang, Z. Wang, H. Gao, S. Swift, and J. Kurths, A constrained evolutionary computation method for detecting controlling regions of cortical networks [J], IEEE/ACM Trans. Comput. Biol. Bioinf.,2012,9:1569-1581.
[59]C.-I Chen, A phasor estimator for synchronization between power grid and distributed generation system [J], IEEE Trans. Ind. Electron.,2013,60: 3248-3255.
[60]Y. Wang, and F. J. Doyle, Exponential synchronization rate of kuramoto oscillators in the presence of a pacemaker [J], IEEE Trans. Automatic Control, 2013,58:989-994.
[61]Y.-C. Liu and N. Chopra, Controlled synchronization of heterogeneous robotic manipulators in the task space [J], IEEE Trans. Robotics,2012,28:268-275.
[62]Y Xiao and K. Y. Zhu, Optimal synchronization control of high-precision motion systems [J], IEEE Trans. Ind. Electron.,2006,53:1160-1169.
[63]D. Sun, X. Shao, and G. Feng, A model-free cross-coupled control for position synchronization of multi-axis motions:theory and experiments [J], IEEE Trans. ControlSyst. Technol.,2007,15:306-314.
[64]Z. Zhang, K. T. Chau, and Z. Wang, Chaotic speed synchronization control of multiple induction motors using stator flux regulation [J], IEEE Trans. Magn., 2012,48:4487-4490.
[65]方一鸣,赵琳琳,欧发顺,冷带轧机两侧压下位置系统鲁棒动态输出反馈同步控制[J],自动化学报,2009,35(4)：438-442.
[66]Y Liu, Z. Wang, X. Liu, Exponential synchronization of complex networks with Markovian jump and mixed delays [J], Phys. Lett. A,2008,372:3986-3998.
[67]M. Yang, Y.-W. Wang, J.-W. Xiao, and H. O. Wang, Robust synchronization of impulsively-coupled complex switched networks with parametric uncertainties and time-varying delays [J], Nonlinear Anal. RWA,2010,11:3000-3020.
[68]Y. Wang, Z. D. Wang, J. L. Liang, A delay fractioning approach to global synchronization of delayed complex networks with stochastic disturbances [J], Phys. Lett. A,2008,372:6066-6073.
[69]W. Lu and T. Chen, Synchronisation in complex networks of coupled systems with directed topologies [J], Int. J. Syst. Sci.,2009,40:909-921.
[70]J. Liang, Z. Wang, X. Liu, and P. Louvieris, Robust synchronization for two-dimensional discrete-time coupled dynamical networks [J], IEEE Trans. Neural Netw.Learn. Syst.,2012,23:942-953.
[71]J. Zhou, J. Lu, and J. Lu, Adaptive synchronization of an uncertain complex dynamical network [J], IEEE Trans. Automatic Control,2006,51:652-656.
[72]S. Bowong, Adaptive synchronization between two different chaotic dynamical systems [J], Commun. Nonlinear Sci. Numer. Simul,2007,12:976-985.
[73]X.-L. Li, J.-D. Cao, Adaptive synchronization for delayed neural networks with stochastic perturbation [J], J. Franklin Inst.,2008,345:779-791.
[74]Q. Zhang, J. Lu, J. Lu, and Chi K. Tse, Adaptive feedback synchronization of a general complex dynamical network with delayed nodes [J], IEEE Trans. Circuits Syst. Ⅱ, Exp. Briefs,2008,55:183-187.
[75]J. Lu, D.W.C. Ho, and J. Cao, Synchronization in an array of nonlinearly coupled chaotic neural networks with delay coupling [J], Int. J. Bifurcation Chaos,2008,18:3101-3111.
[76]L.-H. Nguyen, K.-S. Hong, Adaptive synchronization of two coupled chaotic Hindmarsh-Rose neurons by controlling the membrane potential of a slave neuron [J], Appl. Math. Model.,2013,37:2460-2468.
[77]H. Su, Z. Rong, Michael Z. Q. Chen, X. Wang, G. Chen, and H. Wang, Decentralized adaptive pinning control for cluster synchronization of complex dynamical networks [J], IEEE Trans. Cybern.,2013,43:394-399.
[78]俞立,吴玉书,宋洪波,具有随机长时延的网络控制系统保性能控制[J],控制理论与应用,2010,27(8)：985-990.
[79]岳东,彭晨,Qing long Han,网络控制系统的分析与综合[M],北京：科学出版社,2007.
[80]D. F. Delchumps. Stabilizing a linear system with quantized state feedback [J]. IEEE Trans. Autom. Control,1990,35(8):916-924.
[81]Bo Shen, Zidong Wang, Huisheng Shu, Guoliang Wei, Robust H∞ finite-horizon filtering with randomly occurred nonlinearities and quantization effects, Automatica,2010,46:1743-1751.
[82]Z. Zuo, D. W. Ho, Y. Wang. Fault tolerant control for singular systems with actuator saturation and nonlinear perturbation [J]. Automatica,2010,46(3): 569-576.
[83]J. Wu, X. Chen and H. Cao, H∞ filtering with stochastic sampling [J]. Signal Process.,2010,90(4):1131-1145.
[84]Zidong Wang, Fuwen Yang, Daniel W. C. Ho, Xiaohui Liu, Robust H∞ filtering for stochastic time-delay systems with missing measurements [J]. IEEE Trans. Signal Process.,2006,54(7):2579-2587.
[85]E. T. Jeung, D. C. Oh, J. H. Kim and H. B. Park, Robust controller design for uncertain system with time delays:LMI approach [J], Automatic,1996, 32(8):1229-1231.
[86]X. Li and G. E. Desouze. Delay-dependent robust stability and stabilization of uncertain linear delay system:A linear matrix inequality approach [J]. IEEE Trans. Autom. Control,1997,42(8):1144-1148.
[87]S. I. Niculescu, C. E. Souza, L. Duard and J. M. Dion, Robust exponential stability of uncertain systems with time-varying delays [J], IEEE Trans. Autom. Control,1998,43(5):743-748.
[88]J. J. Yan, J. S. Tsai and F. C. Kung, A new result on the robust stability of uncertainty systems with time-vary ing delay [J], IEEE Trans. Circuits Syst., 2001,48(7):914-916.
[89]H. Zhang, L. Xie, W. Wang, X. Lu, An innovation approah to H∞ fixed-lag smoothing for continuous time-varying systems [J], IEEE Trans. Automatic Control,2004,49(12):2240-2244.
[90]V. B. Kolmanovskii, V. R. Nosov, Stability of functional differential equations [M], London:Academic Press,1986.
[91]J. K. Hale, S. V. Lunel, Introduction to functional differential equations [M], New York:Springer,1993.
[92]F. Wu, Delay-independent stability of genetic regulatory networks [J], IEEE Trans. Neural Netw.,2011,22(11):1685-1693.
[93]X. Li and R. Rakkiyappan, Delay-dependent global asymptotic stability criteria for stochastic genetic regulatory networks with Markovian jumping parameters [J], Appl. Math. Modelling,2012,36:1718-1730.
[94]C. Lien, K. Yu, Y. Lin, Y. Chung, and L. Chung, Global exponential stability for uncertain delayed neural networks of neutral type with mixed time delays [J], IEEE Trans. Syst., Man, Cybern. B, Cybern.,2008,8:709-720.
[95]L. Huang and X. Mao, Delay-dependent exponential stability of neutral stochastic delay systems [J], IEEE Trans. Automatic Control,2009,54: 147-152.
[96]H. Chen, Y. Zhang, and P. Hu, Novel delay-dependent robust stability criteria for neutral stochastic delayed neural networks [J], Neurocomputing,2010,73, 2554-2561.
[97]H. Zhang, Z. Liu, and G. Huang, Novel delay-dependent robust stability analysis for switched neutral-type neural networks with time-varying delays via SC technique [J], IEEE Trans. Syst., Man, Cybern. B, Cybern.,2010,40: 1480-1491.
[98]Hongli Dong, Zidong Wang, Daniel W. C. Ho, Huijun Gao, Variance-constrained H∞ filtering for a class of nonlinear time-varying systems with multiple missing measurements:the finite-horizon case [J]. IEEE Trans. Signal Process.,2010,58(5):2534-2543.
[99]Hongli Dong, Zidong Wang, and Huijun Gao, H∞ Fuzzy Control for Systems With Repeated Scalar Nonlinearities and Random Packet Losses [J], IEEE Trans. Fuzzy Syst.,2009,17(2):440-450.
[100]Zidong Wang, Daniel W.C. Ho, Yurong Liu, Xiaohui Liu, Robust H∞ control for a class of nonlinear discrete time-delay stochastic systems with missing measurements [J], Automatica,2009,45:684-691.
[101]P. Shi, M. Mahmoud, S. K. Nguang, A. Ismail. Robust filtering for jumping systems with mode-dependent delays [J]. Signal Processing,2006,86(1): 140-162.
[102]E. Fridman, M. Dambrine, Control under quantization, saturation and delay:an LMI approach [J], Automatica,2009,45(10):2258-2264.
[103]E. Fridmana, A. Seuretb, J.-P. Richard, Robust sampled-data stabilization of linear systems:an input delay approach [J], Automatica,2004,40(8): 1441-1446.
[104]丁永生,计算智能的新框架：生物网络结构[J],智能系统学报,2007,2(2)：26-30.
[105]S. Viollet, N. Franceschini, A high speed gaze control system based on the Vestibulo-Ocular Reflex [J], Robotics and Autonomous Systems,2005,50(4): 147-161.
[106]朱大年,生理学[M],北京：人民卫生出版社,2008.
[107]Y.-S. Ding, B. Liu, L.-H. Ren, Intelligent decoupling control system inspired from modulation of the growth hormone in neuroendocrine system [J], Dynam. Cont. Dis. Ser. B,2007,14(5):679-693.
[108]B. Liu, L.-H. Ren, Y.-S. Ding, A novel intelligent controller based on modulation of neuroendocrine system [J]. Lect. Notes Comput. Sei.,2005, 3498(3):119-124.
[109]王飞跃,平行控制：数据驱动的计算控制方法[J],自动化学报,2013,39(4)：293-302.
[110]] J.-H. Xia, C.-Y. Liu, B.-S. Tang, Q. Pan, L. Huang, H.-P. Dai, B.-R. Zhang, W. Xie, D.-X. Hu, D. Zheng, X.-L. Shi, D.-A. Wang, K. Xia, K.-P. Yu, X.-D. Liao, Y Feng, Y.-F. Yang, J.-Y X, D.-H. Xie, J.-Z. Huang, Mutations in the gene encoding gap junction protein β - 3 associated with autosomal dominant hearing impairment [J], Nature Genetics,1998,20:370-373.
[111]E. Marshall, Genome sequencing-Clinton and blair back rapid release of data [J], Science,2000,287(5460):1903-1903.
[112]Eric H. Davidson and Douglas H. Erwin, Gene regulatory networks and the evolution of animal body plans [J], Science, vol.311,796-800,2006.
[113]P. Smolen, D. A. Baxter, and J. H. Byrne, Mathematical modeling of gene networks [J], Neuron,2000,26:567-580.
[114]H. De Jong, Modelling and simulation of genetic regulatory systems:A literature review [J], J. Comput. Biol.,2002,9:67-103.
[115]C.J. Tomlin and J.D. Axelrod, Biology by numbers:Mathematical modelling in developmental biology [J], Nat. Rev. Genet.,2007,8:331-340.
[116]T. Schlitt and A. Brazma, Current approaches to gene regulatory network modeling [J], BMC Bioinformatics,2007,8 s9.
[117]G. Chesi, Robustness analysis of genetic regulatory networks affected by model uncertainty [J], Automatica,2011,47:1131-1138.
[118]Q. Ma, S. Xu, Y. Zou, and J. Lu, Robust stability for discrete-time stochastic genetic regulatory networks [J], Nonlinear Anal-Real,2011,12:2586-2595.
[119]P. Li and J. Lam, Disturbance analysis of nonlinear differential equation models of genetic SUM regulatory networks [J], IEEE/ACM Trans. Comput. Biol. Bioinform [J],2011,8:2586-2595.
[120]Z. Wang, X. Liao, S. Guo, and G. Liu, Stability analysis of genetic regulatory network with time delays and parameter uncertainties [J], IET Control Theory Appl,2010,4:2018-2028.
[121]W. Pan, Z. Wang, and J. Hu, Robust stability of delayed genetic regulatory networks with different sources of uncertainties [J], Asian J. Control,2011, 13(5):645-654.
[122]Q. Ye and B. Cui, Mean square exponential and robust stability of stochastic discrete-time genetic regulatory networks with uncertainties [J], Cogn Neurodyn, 2010,4:165-176.
[123]Z. Wang, Y. Liu, X. Liu, and Y. Shi, Robust state estimation for discrete-time stochastic neural networks with probabilistic measurement delays [J], Neurocomputing,2010,74:256-264.
[124]W. Yu, J. LU, G. Chen, Z. Duan, and Q. Zhou, Estimating uncertain delayed genetic regulatory networks:An adaptive filtering approach [J], IEEE Trans. Autom. Control,2009,54(4):256-264.
[125]T. Kobayashi, L. Chen, and K. Aihara, Modeling genetic switches with positive feedback loops [J], J. Theor. Biol,2003,221:379-399.
[126]J. Cao and F. Ren, Exponential stability of discrete-time genetic regulatory networks with delays [J], IEEE Trans. Neural Netw.,2008,19(3):520-523.
[127]B. Lv, J. Liang, and J. Cao, Robust distributed state estimation for genetic regulatory networks with Markovian jumping parameters [J], Commun Nonlinear Set Numer. Simulate 2011,16:4060-4078.
[128]P. Li, J. Lam, and Z. Shu, On the transient and steady-state estimates of interval genetic regulatory networks [J], IEEE Trans. Syst., Man, Cybern. Part B: Cybern.,2010,40(2):336-349.
[129]Y. Zhang, S. Xu, and Z. Zeng, Novel robust stability criteria of discrete-time stochastic recurrent neural networks with time delay [J], Neurocomputing,2009 72:3343-3351.
[130]P. Gahinet, A. Nemirovsky, A. J. Laub, and M. Chilali, LMI control toolbox [M], New York:The Math Works, Inc.,1995.
[131]M. B. Elowitz and S. Leibler, A synthetic oscillatory network of transcriptional regulators [J], Nature,2000,403:335-338.
[132]G. Chesi, On the steady states of uncertain genetic regulatory networks [J], IEEE Trans. Syst., Man, Cybern. Part A,2012,42(4):1020-1024.
[133]Z. Wang, Daniel W. C. Ho, and X. Liu, Variance-constrained filtering for uncertain stochastic systems with missing measurements [J], IEEE Trans. Autom. Control,2003,48(7):1254-1258.
[134]H. Liang and T. Zhou, Robust state estimation for uncertain discrete-time stochastic systems with missing measurement [J], Automatica,2011,47: 1520-1524.
[135]Z. Wang, J. Lam, G. Wei, K. Fraser, and X. Liu, Filtering for nonlinear genetic regulatory networks with stochastic disturbances [J], IEEE Trans. Autom. Control,2008,53(10):2448-2457.
[136]Y. Sun, G. Feng, and J. Cao, Robust stochastic stability analysis of genetic regulatory networks with disturbance attenuation [J], Neurocomputing,2012,79: 39-49.
[137]W. Wang, and S. Zhong, Stochastic stability analysis of uncertain genetic regulatory networks with mixed time-vary ing delays [J], Neurocomputing,2012 82:143-156.
[138]G. Wei, Z. Wang, B. Shen, and M. Li, Probability-dependent gain-scheduled filtering for stochastic systems with missing measurements [J], IEEE Trans. Circuits Syst. Ⅱ,2011,58(11):753-757.
[139]L. Ma, F. Da, and K. Zhang, Exponential H∞ filter design for discrete time-delay stochastic systems with markovian jump parameters and missing measurements [J], IEEE Trans. Circuits Syst.I,2011,58(5):994-1007.
[140]C. Li, L. Chen, and Kazuyuki Aihara, Stochastic stability of genetic networks with disturbance attenuation [J], IEEE Trans. Circuits Syst. Ⅱ,2007,54(10): 892-896.
[141]B. Shen, Z. Wang, J. Liang and X. Liu, Sampled-data H∞ filtering for stochastic genetic regulatory networks [J], Int. J. Robust. Nonlinear Control, 2011,21(15):1759-1777.
[142]Y. Xia, C. Sun, and W. Zheng, Discrete-time neural network for fast solving large linear L1 estimation problems and its application to image restoration [J], IEEE Trans. Neural Netw. Learn. Syst.,2012,23:812-820.
[143]K. Burse, R. N. Yadav, and S. C. Shrivastava, Channel equalization using neural networks:a review [J], IEEE Trans. Syst., Man, Cybern. C, Appl. Rev.,2010,40: 352-357.
[144]T. H. Oong and N. A. M. Isa, Adaptive evolutionary artificial neural networks for pattern classification [J], IEEE Trans. Neural Netw.,2011,22:1823-1836.
[145]A.-M Zou, K.-D Kumar, and Z.-G Hou, Quaternion-based adaptive output feedback attitude control of spacecraft using Chebyshev Neural Networks [J], IEEE Trans. Neural Netw.,2010,21:1457-1471.
[146]Y. Shen and J. Wang, Robustness analysis of global exponential stability of recurrent neural networks in the presence of time delays and random disturbances [J], IEEE Trans. Neural Netw. Learn. Syst.,2012,23:87-96.
[147]S. Blythe, X. Mao, and A. Shah, Razumikhin-type theorems on stability of stochastic neural networks with delays [J], Stochastic Analysis and Applications, 2001,19:85-101.
[148]P. Balasubramaniam and R. Rakkiyappan, Global asymptotic stability of stochastic recurrent neural networks with multiple discrete delays and unbounded distributed delays [J], Applied Mathematics and Computation,2008, 204:680-686.
[149]Y. Chen and W. Zheng, Stability and L2 performance analysis of stochastic delayed neural networks [J], IEEE Tram. Neural Netw.,2011,22:1662-1668.
[150]Y. Shen and J. Wang, Almost sure exponential stability of recurrent neural networks with markovian switching [J], IEEE Trans. Neural Netw.,2009,20: 840-855.
[151]M. Luo, S. Zhong, R. Wang, and W. Kang, Robust stability analysis for discrete-time stochastic neural networks systems with time-varying delays [J], Applied Mathematics and Computation,2009,209:305-313.
[152]R. Yang, Z. Zhang, and P. Shi, Exponential stability on stochastic neural networks with discrete interval and distributed delays [J], IEEE Trans. Neural Netw.,2010,21:169-175.
[153]W. Chen and W. Zheng, Robust stability analysis for stochastic neural networks with time-varying delay [J], IEEE Trans. Neural Netw.,2010,21:508-514.
[154]O. Faydasicok and S. Arik, Robust stability analysis of a class of neural networks with discrete time delays [J], Neural Netw.,2012,29-30:52-59.
[155]X. Li and X. Zhu, Stability analysis of neutral systems with distributed delays [J], Automatica,2008,44,2197-2201.
[156]R. Rakkiyappan, P. Balasubramaniam, J. Cao, Global exponential stability results for neutral-type impulsive neural networks [J], Nonlinear Anal. RWA, 2010,11:122-130.
[157]T. Vyhlidal and P. Zitek, Modification of Mikhaylov criterion for neutral time-delay systems [J], IEEE Trans. Automatic Control,2009,54:2430-2435.
[158]Z. Wang, J. Lam, and K. J. Burnham, Stability analysis and observer design for neutral delay systems [J], IEEE Trans. Automatic Control,2002,47:478-483.
[159]R. Rakkiyappan and P. Balasubramaniam, LMI conditions for global asymptotic stability results for neutral-type neural networks with distributed time delays [J], Appl. Math. Comput.,2008,204:317-324.
[160]Z. Zhang, W. Liu, and D. Zhou, Global asymptotic stability to a generalized Cohen-Grossberg BAM neural networks of neutral type delays [J], Neural Netw., 2012,25:94-105.
[161]Y. He, Q. Wang, M. Wu, and C. Lin, Delay-dependent state estimation for delayed neural networks, IEEE Trans. Neural Netw.,2006,7:1077-1081.
[162]Y. Liu, Z. Wang, and X. Liu, Design of exponential state estimators for neural networks with mixed time delays [J], Physics letters A,2007,364:401-412.
[163]H. Huang, G. Feng, and J. Cao, Robust state estimation for uncertain neural networks with time-varying delay [J], IEEE Trans. Neural Netw.,2008,19: 1329-1339.
[164]H. Bao and J. Cao, Delay-distribution-dependent state estimation for discrete-time stochastic neural networks with random delay [J], Neural Networks,2011,24:19-28.
[165]Y. Chen and W. Zheng, Stochastic state estimation for neural networks with distributed delays and Markovian jump [J], Neural Networks,2012,25:14-20.
[166]Ju H. Park and O.M. Kwon, Design of state estimator for neural networks of neutraltype [J], Appl. Math. Comput.,2008,202:360-369.
[167]Ju H. Park and O.M. Kwon, Further results on state estimation for neural networks of neutral-type with time-varying delay [J], Appl. Math. Comput., 2009,208:69-75.
[168]Ju H. Park, O.M. Kwon, and S.M. Lee, State estimation for neural networks of neutraltype with interval time-varying delays [J], Appl. Math Comput.,2008, 203:217-223.
[169]T. Li, A. Song and S. Fei, Further stability analysis on Cohen-Grossberg neural networks with time-vary ing and distributed delays [J], Int. J. Syst. Sci.,2011,42: 1639-1649.
[170]Z. Wu, P., Shi H. Su and J. Chu, State estimation for discrete-time neural networks with time-varying delay [J], Int. J. Syst. Sci.,2012,43:647-655.
[171]Y. Liu, Z. Wang, J. Liang, and X. Liu, Stability and synchronization of discrete-time Markovian jumping neural networks with mixed mode-dependent time-delays [J], IEEE Trans. Neural Netw.,2009,20:1102-1116.
[172]X. Liang, Y.-S. Ding, Z.-D. Wang, K.-R. Hao, and H.-P. Wang, Bidirectional optimization of the melting spinning process [J], IEEE Trans. Cybern.,2013, in press.
[173]G. Zhang, T., Wang T. Li, and S. Fei, Delay-derivative-dependent stability criterion for neural networks with probabilistic time-varying delay [J], Int. J. Syst. Sci.,2012:1-12, iFirst.
[174]L Sheng. and M. Gao, Robust stability of Markovian jump discrete-time neural networks with partly unknown transition probabilities and mixed mode-dependent delays [J], Int. J. Syst. Sci.,2013,44:252-264.
[175]Y. Wang, Z. Cai,G. Guo, Y. Zhou, Multiobjective optimization and hybrid evolutionary algorithm to solve constrained optimization problems [J], IEEE Trans. Syst., Man, Cybern., B, Cybern.,2007,37(3):560-575.
[176]J. Horn, N.s Nafpliotis, and D. E. Goldberg, A niched pareto genetic algorithm for multiobjective optimization [C], Proceedings of the First IEEE Conference on Evolutionary Computation, Orlando FL,1994:82-87.
[177]N. Srinivas, Kalyanmoy Deb, Multiobjective optimization using nondominated sorting in genetic algorithms, Evol. Comput.,1994,2(3):221-248.
[178]Steven H. Strogatz, Exploring complex networks [J], Nature,2001,410: 268-276.
[179]M. E. J. Newman, The structure and function of complex networks [J], SIAM Rev.,2003,45,167-256.
[180]J. Ltl and G. Chen, A time-varying complex dynamical network model and its controlled synchronization criteria [J], IEEE Trans. Automatic Control,2005,50: 841-846.
[181]W. Yu, G. Chen, and M. Cao, Consensus in directed networks of agents with nonlinear dynamics [J], IEEE Trans. Automatic Control,2011,56:1436-1441.
[182]Z. Li and G. Chen, Global synchronization and asymptotic stability of complex dynamical networks [J], IEEE Trans. Circuits Syst. Ⅱ, Exp. Briefs,2006,53: 28-33.
[183]Y.-Q. Fan, Y.-H. Wang, Y. Zhang, Q.-R. Wang, The synchronization of complex dynamical networks with similar nodes and coupling time-delay [J], Appl. Math. Comput.,2013,219:6719-6728.
[184]J. Zhou, Q. Wu, L. Xiang, S. Cai, Z. Liu, Impulsive synchronization seeking in general complex delayed dynamical networks [J], Nonlinear Anal. Hybrid Syst., 2011,5:513-514.
[185]J. Lu, D.W.C. Ho, J. Cao, J. Kurths, Single impulsive controller for globally exponential synchronization of dynamical networks [J], Nonlinear Anal. RWA, 2013,14:581-593.
[186]B. Shen, Z. Wang, and X. Liu, Sampled-data synchronization control of dynamical networks with stochastic sampling [J], IEEE Trans. Automatic Control,2012,57:2644-2650.
[187]Z.-H. Guan, Z.-W. Liu, G. Feng, and Y.-W. Wang, Synchronization of complex dynamical networks with time-varying delays via impulsive distributed control [J], IEEE Trans. Circuits Syst. I, Reg. Papers,2010,57:2182-2195.
[188]Vu N. Phat, J.-M. Jiang, A. V. Savkin, I. R. Petersen, Robust stabilization of linear uncertain discrete-time systems via a limited capacity communication channel [J], Syst. Control Lett.,2004,53:347-360.
[189]L. Zhou, G.-P. Lu, Detection and stabilization for discrete-time descriptor systems via a limited capacity communication channel [J], Automatica,2009,45: 2272-2277.
[190]Z.-G. Wu, P. Shi, H.-Y. Su, and J. Chu, Sampled-data synchronization of chaotic Lur'e systems with time delays [J], IEEE Trans. Neural Netw.Learn. Syst.,2013, 24:410-421.
[191]H. K. Lam, and L. D. Seneviratne, Chaotic synchronization using sampled-data fuzzy controller based on fuzzy-model-based approach [J], IEEE Trans. Circuits Syst. I, Reg. Papers,2008,55:883-892.
[192]Y.-W. Wang, J.-W. Xiao, C.-Y. Wen and Z.-H. Guan, Synchronization of continuous dynamical networks with discrete-Time communications [J], IEEE Trans. NeuralNetw.,2011,22:1979-1986.
[193]J. Almeida, C. Silvestre, A. M. Pascoal, Continuous-time consensus with discrete-time communications [J], Syst. Control Lett.,2012,61:788-796.
[194]J. Xiao, Y. Yang, and J. Long, Synchronisation of complex networks with derivative coupling via adaptive control [J], Int. J. Syst. Sci.,2013,44: 2183-2189.
[195]G. Wang, Y. Shen, Cluster synchronisation of directed complex dynamical networks with nonidentical nodes via pinning control [J], Int. J. Syst. Sci.,2013, 44:1577-1586.
[196]S. Li, W. Tang, and J. Zhang, Guaranteed cost control of synchronisation for uncertain complex delayed networks [J], Int. J. Syst. Sci.,2012,43:566-575.
[197]D. Ding, Z. Wang, B. Shen, and H. Shu, H-infinity state estimation for discrete-time complex networks with randomly occurring sensor saturations and randomly varying sensor delays [J], IEEE Trans. Neural Netw.Learn. Syst., 2012,23:725-736.
[198]Y. Tang, H. Gao, W. Zou, and J. Kurths, Distributed synchronization in networks of agent systems with nonlinearities and random switchings, IEEE Trans. Cybern.,2013,43:358-370.
[199]X. Q. Wu, W. X. Zheng, J. Zhou, Generalized outer synchronization between complex dynamical networks [J], Chaos,2009,19(1):013109.
[200]Chieh-Li Chen, Kuo-Ming Chang and Chih-Ming Chang, Modeling and control of a web-fed machine [J], Appl. Math. Model,2004,28:863-876.
[201]Hakan Koc, Dominique Knittel, Michel de Mathelin, and Gabriel Abba, Modeling and robust control of winding systems for elastic webs [J], IEEE Trans. Control Syst. Technol,2002,10(2):197-208.
[202]P. R. Pagilla, N. B. Siraskar, and R. V. Dwivedula, Decentralized control of web processing lines [J], IEEE Trans. Control Syst. Technol,2007,15(1):106-117,.
[203]Enrico Canuto and Fabio Musso, Embedded model control:application to web winding [J], ISA Trans.,2007,46:379-390.
[204]Xijiang Dou and Wilson Wang, Robust control of multistage printing systems [J], Control Eng. Pract.,2010,18:219-229.
[205]Sung-Ho Hur, Reza Katebi, and Andrew Taylor, Modeling and control of a plastic film manufacturing web process [J], IEEE Trans. Ind. Inf.,2011,7(2): 171-178.
[206]Hyun-Kyoo Kang, Chang-Woo Lee, Kee-Hyun Shin, and Sang-Chul Kim, Modeling and matching design of a tension controller using pendulum dancer in roll-to-roll systems [J], IEEE Trans. Ind. Appl,2011,47(4):1558-1566.
[207]Vincent Gassmann, Dominique Knittel, Prabhakar R. Pagilla and Marie-Ange Bueno, Fixed-Order H∞ Tension Control in the Unwinding Section of a Web Handling System Using a Pendulum Dancer [J], IEEE Trans. Control Syst. Technol.,2012,20(1):173-180.
[208]David Kuhm, Dominique Knittel, Marie-Ange Bueno, Robust control strategies for an electric motor driven accumulator with elastic webs [J], ISA Trans.,2012, 51:732-742.
[209]Dong Wang, Jianliang Wang, and Wei Wang, H∞ Ccontroller design of networked control systems with markov packet dropouts [J], IEEE Trans. Syst. Man Cybern., Syst.,2013,43(3):689-697.
[210]N. R. Abjadi, J. Soltain, J. Askari, and G. R. A. Markadeh, Nonlinear sliding-mode control of a multi-motor web-winding system without tension control [J], IET Control Theor. Appl.,2009,3(4):419-427.
[211]Enrico Zio, and Giovanni Sansavini, Vulnerability of smart grids with variable generation and consumption:a system of systems perspective [J], IEEE Trans. Syst. Man Cybern., Syst.,2013,43(3):477-487,.
[212]Xiaoe Ruan, Z. Zenn Bien, and Kwang-Hyun Park, Decentralized iterative learning control to large-scale industrial processes for nonrepetitive trajectory tracking [J], IEEE Trans. Syst., Man, Cybern. A, Syst., Humans,2008,38(1): 238-252.
[213]Shohei Taniguchi, Hitoshi Kino, Ryuta Ozawa, Ryota Ishibashi, Mitsunori Uemura, Katsuya Kanaoka, and Sadao Kawamura, Inverse dynamics of human passive motion based on iterative learning control [J], IEEE Trans. Syst., Man, Cybern. A, Syst., Humans,2012,42(2):307-315.
[214]Xiao Liang, Yong-Sheng Ding, Li-Hong Ren, Kuang-Rong Hao, Hua-Ping Wang, and Jia-Jia Chen, A bio-inspired multilayered intelligent cooperative controller for stretching process of fiber production [J], IEEE Trans. Syst., Man Cybern. C, Appl. Rev.,2012,42(3):367-377.
[215]Xiao Liang, Yongsheng Ding, Lihong Ren, Kuangrong Hao, and Yanling Jin, Data-driven cooperative intelligent controller based on the endocrine regulation mechanism [J], IEEE Trans. Control Syst. Technol., in press.
[216]Hongliang Guo, Yan Meng, and Yaochu Jin, A cellular mechanism for multi-robot construction via evolutionary multi-objective optimization of a gene regulatory network [J], BioSystems,2009,98:193-203,.
[217]Yaochu Jin, Hongliang Guo, and Yan Meng, A hierarchical gene regulatory network for adaptive multirobot pattern formation [J], IEEE Trans. Syst., Man, Cybern., B, Cybern.,2012,42(3):805-816.
[218]Yan Meng, Hongliang Guo, and Yaochu Jin, A morphogenetic approach to flexible and robust shape formation for swarm robotic systems [J], Robot Aoton. Syst.,2013,61:25-38,.
[219]Ping Zhou, Tianyou Chai, and JingSun, Intelligence-based supervisory control for optimal operation of a DCS-controlled grinding system [J], IEEE Trans. Control Syst. Technoi.,2013,21(1):162-175,.
[220]Graziano Chesi, Robustness analysis of genetic regulatory networks affected by model uncertainty [J], Automatica,2011,47:1131-1138.
[221]Yutaka Hori, Tae-Hyoung Kim, and Shinji Hara, Existence criteria of periodic oscillations in cyclic gene regulatory networks [J], Automatica,2011,47: 1203-1209.
[222]Filippo Menolascina, Mariodi Bernardo, and Diegodi Bernardo, Analysis, design and implementation of a novel scheme for in-vivo control of synthetic gene regulatory networks [J], Automatica,2011,47:1265-1270.
[223]S. M. Rezaul Hasan, A novel mixed-signal integrated circuit model for DNA-protein regulatory genetic circuits and genetic state machines [J], IEEE Trans. Circuits Syst. I,2008,55(5):1185-1196.
[224]Tong Wang, Yongsheng Ding, Lei Zhang, and Kuangrong Hao, Robust state estimation for discrete-time stochastic genetic regulatory networks with probabilistic measurement delays [J], Neurocomputing,2013,111:1-12.
[225]Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and T. Meyarivan, A fast and elitist multiobjective genetic algorithm:NSGA-II [J], IEEE Trans. Evol. Comput.,2002,6(2):182-197.
[226]M. B. Bazbouz and G. K. Stylios, Novel mechanism for spinning continuous twisted composite nanofiber yarns [J], Eur. Polym. J.,2008, vol.44(1):1-12.
[227]F. Ismail, M. A. Rahman, A. Mustafa, and T. Masuura, The effect of processing conditions on a polyacrylonitrile fiber produced by a solvent-free coagulation process [J], Mater. Sci. Eng. A,2008,485(1-2):251-257.
[228]N. Yusof and A.F. Ismail, Post spinning and pyrolysis processes of polyacrylonitrile (PAN)-based carbon fiber and activated carbon fiber:A review [J], J. Anal. Appl. Pyrol.,2012,93:1-13.
[229]赵明,苏小红,马培军,赵玲玲,复杂多约束UAVs协同目标分配的一种统一建模方法[J],自动化学报,2012,38(12)：2038-2048.
[230]刘朝华,章兢,李小花,张英杰,免疫协同微粒群进化算法的永磁同步电机多参数辨识模型方法[J],自动化学报,2012,38(10)：1698-1708.
[231]杨震,赖英旭,段立娟,李玉鑑,许听,邮件网络协同过滤机制研究[J],自动化学报,2012,38(3)：399-411.
[232]胡庆雷,周稼康,马广富,无需角速度的含通信时延卫星编队飞行自适应姿态协同跟踪控制[J],2012,38(3)：463-468.
[233]王伟,伏锋,陈小杰,王靖,武斌,楚天广,谢广明,复杂网络上的群体群策[J],智能系统学报,2008,3(2)：95-108.
[234]汪小帆,李翔,陈关荣,复杂网络理论及其应用[M],北京：清华大学出版社,2006.
[235]汪小帆,孙金生,王执铨,控制理论在Internet拥塞控制中的应用[J],控制与决策,2002,17(2)：129-134.
[236]Zhisheng Duan, Jinzhi Wang, Guanrong Chen, Lin Huang, Stability analysis and decentralized control of a class of complex dynamical networks [J], Automatic,2008,44(4):1028-1035.
[237]Jun Zhao, David J. Hill, Tao Liu, Stability of dynamical networks with non-identical nodes:A multiple V-Lyapunov function method [J], Automatic, 2011,47:2615-2625.
[238]Zhi-Hong Guan, Shuang-Hua Yang and Jing Yao, Stability analysis and H∞ control for hybrid complex dynamical networks with coupling delays [J], Int. J. Robust. Nonlinear Control,2012,22:205-222.
[239]Bin Liu and Horacio J. Marquez, Uniform stability of discrete delay systems and synchronization of discrete delay dynamical networks via Razumikhin technique [J], IEEE Trans. Circuits Syst. Regul. Pap.,2008,55(9):2795-2805.
[240]Jianquan Lua, Daniel W.C. Ho, Stabilization of complex dynamical networks with noise disturbance under performance constraint [J], Nonlinear Anal.:Real World Appl.,2011,12(4):1974-1984.
[241]W. Zhong, J. D. Stefanovski, G. M. Dimirovski, J. Zhao, Decentralized control and synchronization of time-vary ing complex dynamical network [J], Kybernetika,2009,45(1):151-167.
[2]方洋旺,潘进,随机系统分析与应用[M],西安：西北工业大学出版社,2006.
[3]方洋旺,随机系统最优控制[M],北京：清华大学出版社,2005.
[4]X.-R. Mao, Exponential stability of stochastic differential equations [M], NewYork:Marcel Dekker,1994.
[5]X.-R. Mao, Stochastic differential equations and applications,2nd Edition [M], Horwood,2008.
[6]A. Friedman, Stochastic differential equations and applications [M], Academic Press, Vol.1,1975, VO1.2,1983.
[7]龚光鲁,随机微分方程引论[M],北京：北京大学出版社,1987.
[8]李树英,许茂增,随机系统的滤波与控制[M],北京：国防工业出版社,1991.
[9]胡适耕,随机微分方程[M],北京：科学出版社,2008.
[10]杨玲玲,章云,陈贞丰,不确定非线性系统基于偏差分离的双线性控制,自动化学报,2010,36(10)：1432-1442.
[11]宋立忠,鄢圣茂,杨立秋,不确定系统鲁棒自适应离散变结构控制[J],自动化学报,2011,37(8)：1024-1028.
[12]胡庆雷,马广富,姜野,刘亚秋,三轴稳定挠性卫星姿态机动时变滑模变结构和主动振动控制[J],控制理论与应用,2009,26(2)：122-126.
[13]Salah Bouhouche, Malek Lahreche, Jurgen Bast, Control of heat transfer in continous casting process using neural networks [J], Acta Autom. Sinica,2008, 34(6):701-706.
[14]王良勇,柴天佑,方正,考虑驱动系统动态的机械手神经网络控制及应用[J],自动化学报,2009,35(5)：622-626.
[15]陈刚,柴毅,丁宝苍,魏善碧,电液位置伺服系统的多滑模神经网络控制[J],控制与决策,2009,24(2)：221-225.
[16]朱明超,李英,李元春,姜日花,基于观测器的可重构机械臂分散自适应模糊控制[J],控制与决策,2009,24(3)：429-434.
[17]孙云平,李俊民,李靖,一类非线性参数化系统自适应重复学习控制[J],控 制理论与应用,2009,26(2)：228-232.
[18]贾涛,刘军,钱富才,一类非线性时滞系统的自适应模糊动态面控制[J],自动化学报,2011,37(1)：83-91.
[19]丁海山,毛剑琴,林岩,基于模糊树模型的间接自适应模糊控制[J],自动化学报,2008,34(6)：678-683.
[20]胡志坤,孙岩,姜斌,何静,张昌凡,一种基于最优未知输入观测器的故障诊断方法[J],自动化学报,2013,39(1)：1-6.
[21]王永富,王殿辉,柴天佑,基于状态估计的摩擦模糊建模与鲁棒自适应控制[J],自动化学报,2011,37(2)：245-251.
[22]周聪,肖建,改进强跟踪滤波算法及其在汽车状态估计中的应用[J],自动化学报,2012,38(9)：1520-1527.
[23]文成林,周东华,多尺度估计理论及其应用[M].北京：清华大学出版社,2002.
[24]Mehra R K. On the identification of variances and adaptive Kalman filtering[J]. IEEE Trans. Autom. Control,1970,AC5(2):175-184.
[25]邓自立,李北新,自适应Kalman滤波器及其应用[J],自动化学报,1992,18(4)：408-413.
[26]高媛,邓自立,带相关观测噪声系统的自校正观测融合Kalman滤波器[J],科学技术与工程,2009,9(7)：1669-1677.
[27]揭慧,张明波,邓自立,多传感器ARMA信号自校正分布式融合Kalman滤波器[J],科学技术与工程,2011,11(3)：443-447,460.
[28]梁化勇,周彤,一类空间连接系统的分布式状态估计及其收敛性分析[J],自动化学报,2010,36(5)：720-730.
[29]Petersen I R, Savkin A V, Robust Kalman Filtering for Signal and System with Large Uncertainties [M]. Boston:Birkhauser Boston,1999.
[30]Fuwen Yang, Zidong Wang, and Y. S. Hung, Robust kalman filtering for discrete yime-varying uncertain systems with multiplicative noises [J], IEEE Trans. Autom. Control,2002,47(7):1179-1183.
[31]Zidong Wang, Daniel W. C. Ho, and Xiaohui Liu, State estimation for delayed neural networks [J]. IEEE Trans. Neural Networks,2005,16(1):279-284.
[32]Zidong Wang, H. Unbehauen, Robust H2/H∞-state estimation for systems with wrror variance constraints:the continuous-time case [J], IEEE Trans. Autom. Control,1999,44(5):1061-1065.
[33]Jinling Liang, James Lamb, Zidong Wang, State estimation for Markov-type genetic regulatory networks with delays and uncertain mode transition rates [J]. Phys. Lett. A,2009 (373):4328-4337.
[34]Zidong Wang, Yurong Liu, Xiaohui Liu, State estimation for jumping recurrent neural networks with discrete and distributed delays [J], Neural Networks, 2009(22):41-48.
[35]Jinling Liang, Zidong Wang, Xiaohui Liu. State estimation for coupled uncertain stochastic networks with missing measurements and time-varying delays:the discrete-time case [J]. IEEE Trans. Neural Networks,2009,20(5): 781-793.
[36]Bo Shen, Zidong Wang, Xiaohui Liu, Bounded H∞ synchronization and state estimation for discrete time-varying stochastic complex networks over a finite horizon [J], IEEE Trans. Neural Networks,2011,22(1):145-157.
[37]Jinling Liang, Zidong Wang, Xiaohui Liu, Distributed state estimation for discrete-time sensor networks with randomly varying nonlinearities and missing measurements [J]. IEEE Trans. Neural Networks,2011,22(3):486-496.
[38]Guoliang Wei, Zidong Wang, Huisheng Shu, Robust filtering with stochastic nonlinearities and multiple missing measurements [J], Automatica,2009,45: 836-841.
[39]Lifeng Ma, Zidong Wang, Jun Hu, Yuming Bo, Zhi Guo, Robust variance-constrained filtering for a class of nonlinear stochastic systems with missing measurements [J], Signal Process.,2010,90:2060-2071.
[40]Deok-Jin Lee, Nonlinear estimation and multiple sensor fusion using unscented information filtering [J]. IEEE Signal Process. Lett.,2008,15:861-864.
[41]Andrea Benigni, Junqi Liu, Ferdinanda Ponci, Antonello Monti, Giuditta Pisano, Sara Sulis, Decoupling power system state estimation by means of stochastic collocation [J]. IEEE Trans. Instrum. Meas.,2011,60(5):1623-1632.
[42]Amin Zia, Thia Kirubarajan, James P. Reilly, Derek Yee, Kumaradevan Punithakumar, Shahram Shirani. An EM algorithm for nonlinear state estimation with model uncertainties [J], IEEE Trans. Instrum. Meas.,2008, 56(3):921-936.
[43]H. K. Khalil, Nonlinear systems, third edtion [M], Englewood Cliffs, NJ: Prentice-Hall,2002.
[44]平续斌,丁宝苍,动态输出反馈鲁棒模型预测控制离线算法[J],自动化学报,2013,39(6)：790-798.
[45]Jie Tian, Xue-Jun Xie, Adaptive state-feedback stabilization for more general high-order stochastic nonlinear systems [J], Acta Automatica Sinica,34(9): 1188-1191.
[46]年晓红,曹莉,基于微分对策的最优状态观测器和最优状态反馈控制器的设计[J],自动化学报,2006,32(5)：807-812.
[47]陈东彦,司玉琴,具有饱和状态反馈离散时滞系统的渐进稳定性[J],自动化学报,2008,34(11)：1445-1448.
[48]陶洪峰,胡寿松,蔡俊伟,变时滞T-S模糊系统的L∞静态输出反馈控制[J],自动化学报,2008,34(4)：453-459.
[49]刘月,马树萍,时滞系统的输出反馈滑模控制的一种奇异系统方法[J],自动化学报,2013,39(5)：594-601.
[50]刘磊明,童朝南,武延坤,一种带有动态输出反馈控制器的网络控制系统的Markov跳变模型[J],自动化学报,2009,35(5)：627-631.
[51]李靖,李俊民,陈为胜,随机非线性严格反馈系统的自适应神经网络输出反馈镇定[J],自动化学报,2010,36(3)：450-453.
[52]张利军,杨立新,郭立东,孙立宁,压电陶瓷驱动平台自适应输出反馈控制[J],自动化学报,2012,38(9)：1550-1556.
[53]X. R. Han, Emilia Fridman, Sarah K. Spurgeon, and Chris Edwards, On the design of sliding-mode static-output-feedback controllers for systems with state delay [J], IEEE Trans. Ind Electron.,2009,56(9):3656-3664.
[54]R.E. Mirollo, S.H. Strogatz, Synchronization of pulse-coupled biological oscillators [J], SIAM J. Appl. Math.,1990,50(6):1645-1662.
[55]Z. Li, Z. Duan, G. Chen, and L. Huang, Consensus of multiagent systems and synchronization of complex networks:a unified viewpoint [J], IEEE Trans. Circuits Syst. I, Reg. Papers,2000,57:213-224.
[56]J. Yi, Y. Wang, J. Xiao, Y. Huang, Exponential synchronization of complex dynamical networks with markovian jump parameters and stochastic delays and its application to multi-agent systems [J], Commun Nonlinear Sci Numer Simulat,20tt,18:1175-1192.
[57]Z. Meng, Z. Li, A. V. Vasilakos, and S. Chen, Delay-induced synchronization of identical linear multiagent mystems [J], IEEE Trans. Cybern.,2013,43: 476-489.
[58]Y. Tang, Z. Wang, H. Gao, S. Swift, and J. Kurths, A constrained evolutionary computation method for detecting controlling regions of cortical networks [J], IEEE/ACM Trans. Comput. Biol. Bioinf.,2012,9:1569-1581.
[59]C.-I Chen, A phasor estimator for synchronization between power grid and distributed generation system [J], IEEE Trans. Ind. Electron.,2013,60: 3248-3255.
[60]Y. Wang, and F. J. Doyle, Exponential synchronization rate of kuramoto oscillators in the presence of a pacemaker [J], IEEE Trans. Automatic Control, 2013,58:989-994.
[61]Y.-C. Liu and N. Chopra, Controlled synchronization of heterogeneous robotic manipulators in the task space [J], IEEE Trans. Robotics,2012,28:268-275.
[62]Y Xiao and K. Y. Zhu, Optimal synchronization control of high-precision motion systems [J], IEEE Trans. Ind. Electron.,2006,53:1160-1169.
[63]D. Sun, X. Shao, and G. Feng, A model-free cross-coupled control for position synchronization of multi-axis motions:theory and experiments [J], IEEE Trans. ControlSyst. Technol.,2007,15:306-314.
[64]Z. Zhang, K. T. Chau, and Z. Wang, Chaotic speed synchronization control of multiple induction motors using stator flux regulation [J], IEEE Trans. Magn., 2012,48:4487-4490.
[65]方一鸣,赵琳琳,欧发顺,冷带轧机两侧压下位置系统鲁棒动态输出反馈同步控制[J],自动化学报,2009,35(4)：438-442.
[66]Y Liu, Z. Wang, X. Liu, Exponential synchronization of complex networks with Markovian jump and mixed delays [J], Phys. Lett. A,2008,372:3986-3998.
[67]M. Yang, Y.-W. Wang, J.-W. Xiao, and H. O. Wang, Robust synchronization of impulsively-coupled complex switched networks with parametric uncertainties and time-varying delays [J], Nonlinear Anal. RWA,2010,11:3000-3020.
[68]Y. Wang, Z. D. Wang, J. L. Liang, A delay fractioning approach to global synchronization of delayed complex networks with stochastic disturbances [J], Phys. Lett. A,2008,372:6066-6073.
[69]W. Lu and T. Chen, Synchronisation in complex networks of coupled systems with directed topologies [J], Int. J. Syst. Sci.,2009,40:909-921.
[70]J. Liang, Z. Wang, X. Liu, and P. Louvieris, Robust synchronization for two-dimensional discrete-time coupled dynamical networks [J], IEEE Trans. Neural Netw.Learn. Syst.,2012,23:942-953.
[71]J. Zhou, J. Lu, and J. Lu, Adaptive synchronization of an uncertain complex dynamical network [J], IEEE Trans. Automatic Control,2006,51:652-656.
[72]S. Bowong, Adaptive synchronization between two different chaotic dynamical systems [J], Commun. Nonlinear Sci. Numer. Simul,2007,12:976-985.
[73]X.-L. Li, J.-D. Cao, Adaptive synchronization for delayed neural networks with stochastic perturbation [J], J. Franklin Inst.,2008,345:779-791.
[74]Q. Zhang, J. Lu, J. Lu, and Chi K. Tse, Adaptive feedback synchronization of a general complex dynamical network with delayed nodes [J], IEEE Trans. Circuits Syst. Ⅱ, Exp. Briefs,2008,55:183-187.
[75]J. Lu, D.W.C. Ho, and J. Cao, Synchronization in an array of nonlinearly coupled chaotic neural networks with delay coupling [J], Int. J. Bifurcation Chaos,2008,18:3101-3111.
[76]L.-H. Nguyen, K.-S. Hong, Adaptive synchronization of two coupled chaotic Hindmarsh-Rose neurons by controlling the membrane potential of a slave neuron [J], Appl. Math. Model.,2013,37:2460-2468.
[77]H. Su, Z. Rong, Michael Z. Q. Chen, X. Wang, G. Chen, and H. Wang, Decentralized adaptive pinning control for cluster synchronization of complex dynamical networks [J], IEEE Trans. Cybern.,2013,43:394-399.
[78]俞立,吴玉书,宋洪波,具有随机长时延的网络控制系统保性能控制[J],控制理论与应用,2010,27(8)：985-990.
[79]岳东,彭晨,Qing long Han,网络控制系统的分析与综合[M],北京：科学出版社,2007.
[80]D. F. Delchumps. Stabilizing a linear system with quantized state feedback [J]. IEEE Trans. Autom. Control,1990,35(8):916-924.
[81]Bo Shen, Zidong Wang, Huisheng Shu, Guoliang Wei, Robust H∞ finite-horizon filtering with randomly occurred nonlinearities and quantization effects, Automatica,2010,46:1743-1751.
[82]Z. Zuo, D. W. Ho, Y. Wang. Fault tolerant control for singular systems with actuator saturation and nonlinear perturbation [J]. Automatica,2010,46(3): 569-576.
[83]J. Wu, X. Chen and H. Cao, H∞ filtering with stochastic sampling [J]. Signal Process.,2010,90(4):1131-1145.
[84]Zidong Wang, Fuwen Yang, Daniel W. C. Ho, Xiaohui Liu, Robust H∞ filtering for stochastic time-delay systems with missing measurements [J]. IEEE Trans. Signal Process.,2006,54(7):2579-2587.
[85]E. T. Jeung, D. C. Oh, J. H. Kim and H. B. Park, Robust controller design for uncertain system with time delays:LMI approach [J], Automatic,1996, 32(8):1229-1231.
[86]X. Li and G. E. Desouze. Delay-dependent robust stability and stabilization of uncertain linear delay system:A linear matrix inequality approach [J]. IEEE Trans. Autom. Control,1997,42(8):1144-1148.
[87]S. I. Niculescu, C. E. Souza, L. Duard and J. M. Dion, Robust exponential stability of uncertain systems with time-varying delays [J], IEEE Trans. Autom. Control,1998,43(5):743-748.
[88]J. J. Yan, J. S. Tsai and F. C. Kung, A new result on the robust stability of uncertainty systems with time-vary ing delay [J], IEEE Trans. Circuits Syst., 2001,48(7):914-916.
[89]H. Zhang, L. Xie, W. Wang, X. Lu, An innovation approah to H∞ fixed-lag smoothing for continuous time-varying systems [J], IEEE Trans. Automatic Control,2004,49(12):2240-2244.
[90]V. B. Kolmanovskii, V. R. Nosov, Stability of functional differential equations [M], London:Academic Press,1986.
[91]J. K. Hale, S. V. Lunel, Introduction to functional differential equations [M], New York:Springer,1993.
[92]F. Wu, Delay-independent stability of genetic regulatory networks [J], IEEE Trans. Neural Netw.,2011,22(11):1685-1693.
[93]X. Li and R. Rakkiyappan, Delay-dependent global asymptotic stability criteria for stochastic genetic regulatory networks with Markovian jumping parameters [J], Appl. Math. Modelling,2012,36:1718-1730.
[94]C. Lien, K. Yu, Y. Lin, Y. Chung, and L. Chung, Global exponential stability for uncertain delayed neural networks of neutral type with mixed time delays [J], IEEE Trans. Syst., Man, Cybern. B, Cybern.,2008,8:709-720.
[95]L. Huang and X. Mao, Delay-dependent exponential stability of neutral stochastic delay systems [J], IEEE Trans. Automatic Control,2009,54: 147-152.
[96]H. Chen, Y. Zhang, and P. Hu, Novel delay-dependent robust stability criteria for neutral stochastic delayed neural networks [J], Neurocomputing,2010,73, 2554-2561.
[97]H. Zhang, Z. Liu, and G. Huang, Novel delay-dependent robust stability analysis for switched neutral-type neural networks with time-varying delays via SC technique [J], IEEE Trans. Syst., Man, Cybern. B, Cybern.,2010,40: 1480-1491.
[98]Hongli Dong, Zidong Wang, Daniel W. C. Ho, Huijun Gao, Variance-constrained H∞ filtering for a class of nonlinear time-varying systems with multiple missing measurements:the finite-horizon case [J]. IEEE Trans. Signal Process.,2010,58(5):2534-2543.
[99]Hongli Dong, Zidong Wang, and Huijun Gao, H∞ Fuzzy Control for Systems With Repeated Scalar Nonlinearities and Random Packet Losses [J], IEEE Trans. Fuzzy Syst.,2009,17(2):440-450.
[100]Zidong Wang, Daniel W.C. Ho, Yurong Liu, Xiaohui Liu, Robust H∞ control for a class of nonlinear discrete time-delay stochastic systems with missing measurements [J], Automatica,2009,45:684-691.
[101]P. Shi, M. Mahmoud, S. K. Nguang, A. Ismail. Robust filtering for jumping systems with mode-dependent delays [J]. Signal Processing,2006,86(1): 140-162.
[102]E. Fridman, M. Dambrine, Control under quantization, saturation and delay:an LMI approach [J], Automatica,2009,45(10):2258-2264.
[103]E. Fridmana, A. Seuretb, J.-P. Richard, Robust sampled-data stabilization of linear systems:an input delay approach [J], Automatica,2004,40(8): 1441-1446.
[104]丁永生,计算智能的新框架：生物网络结构[J],智能系统学报,2007,2(2)：26-30.
[105]S. Viollet, N. Franceschini, A high speed gaze control system based on the Vestibulo-Ocular Reflex [J], Robotics and Autonomous Systems,2005,50(4): 147-161.
[106]朱大年,生理学[M],北京：人民卫生出版社,2008.
[107]Y.-S. Ding, B. Liu, L.-H. Ren, Intelligent decoupling control system inspired from modulation of the growth hormone in neuroendocrine system [J], Dynam. Cont. Dis. Ser. B,2007,14(5):679-693.
[108]B. Liu, L.-H. Ren, Y.-S. Ding, A novel intelligent controller based on modulation of neuroendocrine system [J]. Lect. Notes Comput. Sei.,2005, 3498(3):119-124.
[109]王飞跃,平行控制：数据驱动的计算控制方法[J],自动化学报,2013,39(4)：293-302.
[110]] J.-H. Xia, C.-Y. Liu, B.-S. Tang, Q. Pan, L. Huang, H.-P. Dai, B.-R. Zhang, W. Xie, D.-X. Hu, D. Zheng, X.-L. Shi, D.-A. Wang, K. Xia, K.-P. Yu, X.-D. Liao, Y Feng, Y.-F. Yang, J.-Y X, D.-H. Xie, J.-Z. Huang, Mutations in the gene encoding gap junction protein β - 3 associated with autosomal dominant hearing impairment [J], Nature Genetics,1998,20:370-373.
[111]E. Marshall, Genome sequencing-Clinton and blair back rapid release of data [J], Science,2000,287(5460):1903-1903.
[112]Eric H. Davidson and Douglas H. Erwin, Gene regulatory networks and the evolution of animal body plans [J], Science, vol.311,796-800,2006.
[113]P. Smolen, D. A. Baxter, and J. H. Byrne, Mathematical modeling of gene networks [J], Neuron,2000,26:567-580.
[114]H. De Jong, Modelling and simulation of genetic regulatory systems:A literature review [J], J. Comput. Biol.,2002,9:67-103.
[115]C.J. Tomlin and J.D. Axelrod, Biology by numbers:Mathematical modelling in developmental biology [J], Nat. Rev. Genet.,2007,8:331-340.
[116]T. Schlitt and A. Brazma, Current approaches to gene regulatory network modeling [J], BMC Bioinformatics,2007,8 s9.
[117]G. Chesi, Robustness analysis of genetic regulatory networks affected by model uncertainty [J], Automatica,2011,47:1131-1138.
[118]Q. Ma, S. Xu, Y. Zou, and J. Lu, Robust stability for discrete-time stochastic genetic regulatory networks [J], Nonlinear Anal-Real,2011,12:2586-2595.
[119]P. Li and J. Lam, Disturbance analysis of nonlinear differential equation models of genetic SUM regulatory networks [J], IEEE/ACM Trans. Comput. Biol. Bioinform [J],2011,8:2586-2595.
[120]Z. Wang, X. Liao, S. Guo, and G. Liu, Stability analysis of genetic regulatory network with time delays and parameter uncertainties [J], IET Control Theory Appl,2010,4:2018-2028.
[121]W. Pan, Z. Wang, and J. Hu, Robust stability of delayed genetic regulatory networks with different sources of uncertainties [J], Asian J. Control,2011, 13(5):645-654.
[122]Q. Ye and B. Cui, Mean square exponential and robust stability of stochastic discrete-time genetic regulatory networks with uncertainties [J], Cogn Neurodyn, 2010,4:165-176.
[123]Z. Wang, Y. Liu, X. Liu, and Y. Shi, Robust state estimation for discrete-time stochastic neural networks with probabilistic measurement delays [J], Neurocomputing,2010,74:256-264.
[124]W. Yu, J. LU, G. Chen, Z. Duan, and Q. Zhou, Estimating uncertain delayed genetic regulatory networks:An adaptive filtering approach [J], IEEE Trans. Autom. Control,2009,54(4):256-264.
[125]T. Kobayashi, L. Chen, and K. Aihara, Modeling genetic switches with positive feedback loops [J], J. Theor. Biol,2003,221:379-399.
[126]J. Cao and F. Ren, Exponential stability of discrete-time genetic regulatory networks with delays [J], IEEE Trans. Neural Netw.,2008,19(3):520-523.
[127]B. Lv, J. Liang, and J. Cao, Robust distributed state estimation for genetic regulatory networks with Markovian jumping parameters [J], Commun Nonlinear Set Numer. Simulate 2011,16:4060-4078.
[128]P. Li, J. Lam, and Z. Shu, On the transient and steady-state estimates of interval genetic regulatory networks [J], IEEE Trans. Syst., Man, Cybern. Part B: Cybern.,2010,40(2):336-349.
[129]Y. Zhang, S. Xu, and Z. Zeng, Novel robust stability criteria of discrete-time stochastic recurrent neural networks with time delay [J], Neurocomputing,2009 72:3343-3351.
[130]P. Gahinet, A. Nemirovsky, A. J. Laub, and M. Chilali, LMI control toolbox [M], New York:The Math Works, Inc.,1995.
[131]M. B. Elowitz and S. Leibler, A synthetic oscillatory network of transcriptional regulators [J], Nature,2000,403:335-338.
[132]G. Chesi, On the steady states of uncertain genetic regulatory networks [J], IEEE Trans. Syst., Man, Cybern. Part A,2012,42(4):1020-1024.
[133]Z. Wang, Daniel W. C. Ho, and X. Liu, Variance-constrained filtering for uncertain stochastic systems with missing measurements [J], IEEE Trans. Autom. Control,2003,48(7):1254-1258.
[134]H. Liang and T. Zhou, Robust state estimation for uncertain discrete-time stochastic systems with missing measurement [J], Automatica,2011,47: 1520-1524.
[135]Z. Wang, J. Lam, G. Wei, K. Fraser, and X. Liu, Filtering for nonlinear genetic regulatory networks with stochastic disturbances [J], IEEE Trans. Autom. Control,2008,53(10):2448-2457.
[136]Y. Sun, G. Feng, and J. Cao, Robust stochastic stability analysis of genetic regulatory networks with disturbance attenuation [J], Neurocomputing,2012,79: 39-49.
[137]W. Wang, and S. Zhong, Stochastic stability analysis of uncertain genetic regulatory networks with mixed time-vary ing delays [J], Neurocomputing,2012 82:143-156.
[138]G. Wei, Z. Wang, B. Shen, and M. Li, Probability-dependent gain-scheduled filtering for stochastic systems with missing measurements [J], IEEE Trans. Circuits Syst. Ⅱ,2011,58(11):753-757.
[139]L. Ma, F. Da, and K. Zhang, Exponential H∞ filter design for discrete time-delay stochastic systems with markovian jump parameters and missing measurements [J], IEEE Trans. Circuits Syst.I,2011,58(5):994-1007.
[140]C. Li, L. Chen, and Kazuyuki Aihara, Stochastic stability of genetic networks with disturbance attenuation [J], IEEE Trans. Circuits Syst. Ⅱ,2007,54(10): 892-896.
[141]B. Shen, Z. Wang, J. Liang and X. Liu, Sampled-data H∞ filtering for stochastic genetic regulatory networks [J], Int. J. Robust. Nonlinear Control, 2011,21(15):1759-1777.
[142]Y. Xia, C. Sun, and W. Zheng, Discrete-time neural network for fast solving large linear L1 estimation problems and its application to image restoration [J], IEEE Trans. Neural Netw. Learn. Syst.,2012,23:812-820.
[143]K. Burse, R. N. Yadav, and S. C. Shrivastava, Channel equalization using neural networks:a review [J], IEEE Trans. Syst., Man, Cybern. C, Appl. Rev.,2010,40: 352-357.
[144]T. H. Oong and N. A. M. Isa, Adaptive evolutionary artificial neural networks for pattern classification [J], IEEE Trans. Neural Netw.,2011,22:1823-1836.
[145]A.-M Zou, K.-D Kumar, and Z.-G Hou, Quaternion-based adaptive output feedback attitude control of spacecraft using Chebyshev Neural Networks [J], IEEE Trans. Neural Netw.,2010,21:1457-1471.
[146]Y. Shen and J. Wang, Robustness analysis of global exponential stability of recurrent neural networks in the presence of time delays and random disturbances [J], IEEE Trans. Neural Netw. Learn. Syst.,2012,23:87-96.
[147]S. Blythe, X. Mao, and A. Shah, Razumikhin-type theorems on stability of stochastic neural networks with delays [J], Stochastic Analysis and Applications, 2001,19:85-101.
[148]P. Balasubramaniam and R. Rakkiyappan, Global asymptotic stability of stochastic recurrent neural networks with multiple discrete delays and unbounded distributed delays [J], Applied Mathematics and Computation,2008, 204:680-686.
[149]Y. Chen and W. Zheng, Stability and L2 performance analysis of stochastic delayed neural networks [J], IEEE Tram. Neural Netw.,2011,22:1662-1668.
[150]Y. Shen and J. Wang, Almost sure exponential stability of recurrent neural networks with markovian switching [J], IEEE Trans. Neural Netw.,2009,20: 840-855.
[151]M. Luo, S. Zhong, R. Wang, and W. Kang, Robust stability analysis for discrete-time stochastic neural networks systems with time-varying delays [J], Applied Mathematics and Computation,2009,209:305-313.
[152]R. Yang, Z. Zhang, and P. Shi, Exponential stability on stochastic neural networks with discrete interval and distributed delays [J], IEEE Trans. Neural Netw.,2010,21:169-175.
[153]W. Chen and W. Zheng, Robust stability analysis for stochastic neural networks with time-varying delay [J], IEEE Trans. Neural Netw.,2010,21:508-514.
[154]O. Faydasicok and S. Arik, Robust stability analysis of a class of neural networks with discrete time delays [J], Neural Netw.,2012,29-30:52-59.
[155]X. Li and X. Zhu, Stability analysis of neutral systems with distributed delays [J], Automatica,2008,44,2197-2201.
[156]R. Rakkiyappan, P. Balasubramaniam, J. Cao, Global exponential stability results for neutral-type impulsive neural networks [J], Nonlinear Anal. RWA, 2010,11:122-130.
[157]T. Vyhlidal and P. Zitek, Modification of Mikhaylov criterion for neutral time-delay systems [J], IEEE Trans. Automatic Control,2009,54:2430-2435.
[158]Z. Wang, J. Lam, and K. J. Burnham, Stability analysis and observer design for neutral delay systems [J], IEEE Trans. Automatic Control,2002,47:478-483.
[159]R. Rakkiyappan and P. Balasubramaniam, LMI conditions for global asymptotic stability results for neutral-type neural networks with distributed time delays [J], Appl. Math. Comput.,2008,204:317-324.
[160]Z. Zhang, W. Liu, and D. Zhou, Global asymptotic stability to a generalized Cohen-Grossberg BAM neural networks of neutral type delays [J], Neural Netw., 2012,25:94-105.
[161]Y. He, Q. Wang, M. Wu, and C. Lin, Delay-dependent state estimation for delayed neural networks, IEEE Trans. Neural Netw.,2006,7:1077-1081.
[162]Y. Liu, Z. Wang, and X. Liu, Design of exponential state estimators for neural networks with mixed time delays [J], Physics letters A,2007,364:401-412.
[163]H. Huang, G. Feng, and J. Cao, Robust state estimation for uncertain neural networks with time-varying delay [J], IEEE Trans. Neural Netw.,2008,19: 1329-1339.
[164]H. Bao and J. Cao, Delay-distribution-dependent state estimation for discrete-time stochastic neural networks with random delay [J], Neural Networks,2011,24:19-28.
[165]Y. Chen and W. Zheng, Stochastic state estimation for neural networks with distributed delays and Markovian jump [J], Neural Networks,2012,25:14-20.
[166]Ju H. Park and O.M. Kwon, Design of state estimator for neural networks of neutraltype [J], Appl. Math. Comput.,2008,202:360-369.
[167]Ju H. Park and O.M. Kwon, Further results on state estimation for neural networks of neutral-type with time-varying delay [J], Appl. Math. Comput., 2009,208:69-75.
[168]Ju H. Park, O.M. Kwon, and S.M. Lee, State estimation for neural networks of neutraltype with interval time-varying delays [J], Appl. Math Comput.,2008, 203:217-223.
[169]T. Li, A. Song and S. Fei, Further stability analysis on Cohen-Grossberg neural networks with time-vary ing and distributed delays [J], Int. J. Syst. Sci.,2011,42: 1639-1649.
[170]Z. Wu, P., Shi H. Su and J. Chu, State estimation for discrete-time neural networks with time-varying delay [J], Int. J. Syst. Sci.,2012,43:647-655.
[171]Y. Liu, Z. Wang, J. Liang, and X. Liu, Stability and synchronization of discrete-time Markovian jumping neural networks with mixed mode-dependent time-delays [J], IEEE Trans. Neural Netw.,2009,20:1102-1116.
[172]X. Liang, Y.-S. Ding, Z.-D. Wang, K.-R. Hao, and H.-P. Wang, Bidirectional optimization of the melting spinning process [J], IEEE Trans. Cybern.,2013, in press.
[173]G. Zhang, T., Wang T. Li, and S. Fei, Delay-derivative-dependent stability criterion for neural networks with probabilistic time-varying delay [J], Int. J. Syst. Sci.,2012:1-12, iFirst.
[174]L Sheng. and M. Gao, Robust stability of Markovian jump discrete-time neural networks with partly unknown transition probabilities and mixed mode-dependent delays [J], Int. J. Syst. Sci.,2013,44:252-264.
[175]Y. Wang, Z. Cai,G. Guo, Y. Zhou, Multiobjective optimization and hybrid evolutionary algorithm to solve constrained optimization problems [J], IEEE Trans. Syst., Man, Cybern., B, Cybern.,2007,37(3):560-575.
[176]J. Horn, N.s Nafpliotis, and D. E. Goldberg, A niched pareto genetic algorithm for multiobjective optimization [C], Proceedings of the First IEEE Conference on Evolutionary Computation, Orlando FL,1994:82-87.
[177]N. Srinivas, Kalyanmoy Deb, Multiobjective optimization using nondominated sorting in genetic algorithms, Evol. Comput.,1994,2(3):221-248.
[178]Steven H. Strogatz, Exploring complex networks [J], Nature,2001,410: 268-276.
[179]M. E. J. Newman, The structure and function of complex networks [J], SIAM Rev.,2003,45,167-256.
[180]J. Ltl and G. Chen, A time-varying complex dynamical network model and its controlled synchronization criteria [J], IEEE Trans. Automatic Control,2005,50: 841-846.
[181]W. Yu, G. Chen, and M. Cao, Consensus in directed networks of agents with nonlinear dynamics [J], IEEE Trans. Automatic Control,2011,56:1436-1441.
[182]Z. Li and G. Chen, Global synchronization and asymptotic stability of complex dynamical networks [J], IEEE Trans. Circuits Syst. Ⅱ, Exp. Briefs,2006,53: 28-33.
[183]Y.-Q. Fan, Y.-H. Wang, Y. Zhang, Q.-R. Wang, The synchronization of complex dynamical networks with similar nodes and coupling time-delay [J], Appl. Math. Comput.,2013,219:6719-6728.
[184]J. Zhou, Q. Wu, L. Xiang, S. Cai, Z. Liu, Impulsive synchronization seeking in general complex delayed dynamical networks [J], Nonlinear Anal. Hybrid Syst., 2011,5:513-514.
[185]J. Lu, D.W.C. Ho, J. Cao, J. Kurths, Single impulsive controller for globally exponential synchronization of dynamical networks [J], Nonlinear Anal. RWA, 2013,14:581-593.
[186]B. Shen, Z. Wang, and X. Liu, Sampled-data synchronization control of dynamical networks with stochastic sampling [J], IEEE Trans. Automatic Control,2012,57:2644-2650.
[187]Z.-H. Guan, Z.-W. Liu, G. Feng, and Y.-W. Wang, Synchronization of complex dynamical networks with time-varying delays via impulsive distributed control [J], IEEE Trans. Circuits Syst. I, Reg. Papers,2010,57:2182-2195.
[188]Vu N. Phat, J.-M. Jiang, A. V. Savkin, I. R. Petersen, Robust stabilization of linear uncertain discrete-time systems via a limited capacity communication channel [J], Syst. Control Lett.,2004,53:347-360.
[189]L. Zhou, G.-P. Lu, Detection and stabilization for discrete-time descriptor systems via a limited capacity communication channel [J], Automatica,2009,45: 2272-2277.
[190]Z.-G. Wu, P. Shi, H.-Y. Su, and J. Chu, Sampled-data synchronization of chaotic Lur'e systems with time delays [J], IEEE Trans. Neural Netw.Learn. Syst.,2013, 24:410-421.
[191]H. K. Lam, and L. D. Seneviratne, Chaotic synchronization using sampled-data fuzzy controller based on fuzzy-model-based approach [J], IEEE Trans. Circuits Syst. I, Reg. Papers,2008,55:883-892.
[192]Y.-W. Wang, J.-W. Xiao, C.-Y. Wen and Z.-H. Guan, Synchronization of continuous dynamical networks with discrete-Time communications [J], IEEE Trans. NeuralNetw.,2011,22:1979-1986.
[193]J. Almeida, C. Silvestre, A. M. Pascoal, Continuous-time consensus with discrete-time communications [J], Syst. Control Lett.,2012,61:788-796.
[194]J. Xiao, Y. Yang, and J. Long, Synchronisation of complex networks with derivative coupling via adaptive control [J], Int. J. Syst. Sci.,2013,44: 2183-2189.
[195]G. Wang, Y. Shen, Cluster synchronisation of directed complex dynamical networks with nonidentical nodes via pinning control [J], Int. J. Syst. Sci.,2013, 44:1577-1586.
[196]S. Li, W. Tang, and J. Zhang, Guaranteed cost control of synchronisation for uncertain complex delayed networks [J], Int. J. Syst. Sci.,2012,43:566-575.
[197]D. Ding, Z. Wang, B. Shen, and H. Shu, H-infinity state estimation for discrete-time complex networks with randomly occurring sensor saturations and randomly varying sensor delays [J], IEEE Trans. Neural Netw.Learn. Syst., 2012,23:725-736.
[198]Y. Tang, H. Gao, W. Zou, and J. Kurths, Distributed synchronization in networks of agent systems with nonlinearities and random switchings, IEEE Trans. Cybern.,2013,43:358-370.
[199]X. Q. Wu, W. X. Zheng, J. Zhou, Generalized outer synchronization between complex dynamical networks [J], Chaos,2009,19(1):013109.
[200]Chieh-Li Chen, Kuo-Ming Chang and Chih-Ming Chang, Modeling and control of a web-fed machine [J], Appl. Math. Model,2004,28:863-876.
[201]Hakan Koc, Dominique Knittel, Michel de Mathelin, and Gabriel Abba, Modeling and robust control of winding systems for elastic webs [J], IEEE Trans. Control Syst. Technol,2002,10(2):197-208.
[202]P. R. Pagilla, N. B. Siraskar, and R. V. Dwivedula, Decentralized control of web processing lines [J], IEEE Trans. Control Syst. Technol,2007,15(1):106-117,.
[203]Enrico Canuto and Fabio Musso, Embedded model control:application to web winding [J], ISA Trans.,2007,46:379-390.
[204]Xijiang Dou and Wilson Wang, Robust control of multistage printing systems [J], Control Eng. Pract.,2010,18:219-229.
[205]Sung-Ho Hur, Reza Katebi, and Andrew Taylor, Modeling and control of a plastic film manufacturing web process [J], IEEE Trans. Ind. Inf.,2011,7(2): 171-178.
[206]Hyun-Kyoo Kang, Chang-Woo Lee, Kee-Hyun Shin, and Sang-Chul Kim, Modeling and matching design of a tension controller using pendulum dancer in roll-to-roll systems [J], IEEE Trans. Ind. Appl,2011,47(4):1558-1566.
[207]Vincent Gassmann, Dominique Knittel, Prabhakar R. Pagilla and Marie-Ange Bueno, Fixed-Order H∞ Tension Control in the Unwinding Section of a Web Handling System Using a Pendulum Dancer [J], IEEE Trans. Control Syst. Technol.,2012,20(1):173-180.
[208]David Kuhm, Dominique Knittel, Marie-Ange Bueno, Robust control strategies for an electric motor driven accumulator with elastic webs [J], ISA Trans.,2012, 51:732-742.
[209]Dong Wang, Jianliang Wang, and Wei Wang, H∞ Ccontroller design of networked control systems with markov packet dropouts [J], IEEE Trans. Syst. Man Cybern., Syst.,2013,43(3):689-697.
[210]N. R. Abjadi, J. Soltain, J. Askari, and G. R. A. Markadeh, Nonlinear sliding-mode control of a multi-motor web-winding system without tension control [J], IET Control Theor. Appl.,2009,3(4):419-427.
[211]Enrico Zio, and Giovanni Sansavini, Vulnerability of smart grids with variable generation and consumption:a system of systems perspective [J], IEEE Trans. Syst. Man Cybern., Syst.,2013,43(3):477-487,.
[212]Xiaoe Ruan, Z. Zenn Bien, and Kwang-Hyun Park, Decentralized iterative learning control to large-scale industrial processes for nonrepetitive trajectory tracking [J], IEEE Trans. Syst., Man, Cybern. A, Syst., Humans,2008,38(1): 238-252.
[213]Shohei Taniguchi, Hitoshi Kino, Ryuta Ozawa, Ryota Ishibashi, Mitsunori Uemura, Katsuya Kanaoka, and Sadao Kawamura, Inverse dynamics of human passive motion based on iterative learning control [J], IEEE Trans. Syst., Man, Cybern. A, Syst., Humans,2012,42(2):307-315.
[214]Xiao Liang, Yong-Sheng Ding, Li-Hong Ren, Kuang-Rong Hao, Hua-Ping Wang, and Jia-Jia Chen, A bio-inspired multilayered intelligent cooperative controller for stretching process of fiber production [J], IEEE Trans. Syst., Man Cybern. C, Appl. Rev.,2012,42(3):367-377.
[215]Xiao Liang, Yongsheng Ding, Lihong Ren, Kuangrong Hao, and Yanling Jin, Data-driven cooperative intelligent controller based on the endocrine regulation mechanism [J], IEEE Trans. Control Syst. Technol., in press.
[216]Hongliang Guo, Yan Meng, and Yaochu Jin, A cellular mechanism for multi-robot construction via evolutionary multi-objective optimization of a gene regulatory network [J], BioSystems,2009,98:193-203,.
[217]Yaochu Jin, Hongliang Guo, and Yan Meng, A hierarchical gene regulatory network for adaptive multirobot pattern formation [J], IEEE Trans. Syst., Man, Cybern., B, Cybern.,2012,42(3):805-816.
[218]Yan Meng, Hongliang Guo, and Yaochu Jin, A morphogenetic approach to flexible and robust shape formation for swarm robotic systems [J], Robot Aoton. Syst.,2013,61:25-38,.
[219]Ping Zhou, Tianyou Chai, and JingSun, Intelligence-based supervisory control for optimal operation of a DCS-controlled grinding system [J], IEEE Trans. Control Syst. Technoi.,2013,21(1):162-175,.
[220]Graziano Chesi, Robustness analysis of genetic regulatory networks affected by model uncertainty [J], Automatica,2011,47:1131-1138.
[221]Yutaka Hori, Tae-Hyoung Kim, and Shinji Hara, Existence criteria of periodic oscillations in cyclic gene regulatory networks [J], Automatica,2011,47: 1203-1209.
[222]Filippo Menolascina, Mariodi Bernardo, and Diegodi Bernardo, Analysis, design and implementation of a novel scheme for in-vivo control of synthetic gene regulatory networks [J], Automatica,2011,47:1265-1270.
[223]S. M. Rezaul Hasan, A novel mixed-signal integrated circuit model for DNA-protein regulatory genetic circuits and genetic state machines [J], IEEE Trans. Circuits Syst. I,2008,55(5):1185-1196.
[224]Tong Wang, Yongsheng Ding, Lei Zhang, and Kuangrong Hao, Robust state estimation for discrete-time stochastic genetic regulatory networks with probabilistic measurement delays [J], Neurocomputing,2013,111:1-12.
[225]Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and T. Meyarivan, A fast and elitist multiobjective genetic algorithm:NSGA-II [J], IEEE Trans. Evol. Comput.,2002,6(2):182-197.
[226]M. B. Bazbouz and G. K. Stylios, Novel mechanism for spinning continuous twisted composite nanofiber yarns [J], Eur. Polym. J.,2008, vol.44(1):1-12.
[227]F. Ismail, M. A. Rahman, A. Mustafa, and T. Masuura, The effect of processing conditions on a polyacrylonitrile fiber produced by a solvent-free coagulation process [J], Mater. Sci. Eng. A,2008,485(1-2):251-257.
[228]N. Yusof and A.F. Ismail, Post spinning and pyrolysis processes of polyacrylonitrile (PAN)-based carbon fiber and activated carbon fiber:A review [J], J. Anal. Appl. Pyrol.,2012,93:1-13.
[229]赵明,苏小红,马培军,赵玲玲,复杂多约束UAVs协同目标分配的一种统一建模方法[J],自动化学报,2012,38(12)：2038-2048.
[230]刘朝华,章兢,李小花,张英杰,免疫协同微粒群进化算法的永磁同步电机多参数辨识模型方法[J],自动化学报,2012,38(10)：1698-1708.
[231]杨震,赖英旭,段立娟,李玉鑑,许听,邮件网络协同过滤机制研究[J],自动化学报,2012,38(3)：399-411.
[232]胡庆雷,周稼康,马广富,无需角速度的含通信时延卫星编队飞行自适应姿态协同跟踪控制[J],2012,38(3)：463-468.
[233]王伟,伏锋,陈小杰,王靖,武斌,楚天广,谢广明,复杂网络上的群体群策[J],智能系统学报,2008,3(2)：95-108.
[234]汪小帆,李翔,陈关荣,复杂网络理论及其应用[M],北京：清华大学出版社,2006.
[235]汪小帆,孙金生,王执铨,控制理论在Internet拥塞控制中的应用[J],控制与决策,2002,17(2)：129-134.
[236]Zhisheng Duan, Jinzhi Wang, Guanrong Chen, Lin Huang, Stability analysis and decentralized control of a class of complex dynamical networks [J], Automatic,2008,44(4):1028-1035.
[237]Jun Zhao, David J. Hill, Tao Liu, Stability of dynamical networks with non-identical nodes:A multiple V-Lyapunov function method [J], Automatic, 2011,47:2615-2625.
[238]Zhi-Hong Guan, Shuang-Hua Yang and Jing Yao, Stability analysis and H∞ control for hybrid complex dynamical networks with coupling delays [J], Int. J. Robust. Nonlinear Control,2012,22:205-222.
[239]Bin Liu and Horacio J. Marquez, Uniform stability of discrete delay systems and synchronization of discrete delay dynamical networks via Razumikhin technique [J], IEEE Trans. Circuits Syst. Regul. Pap.,2008,55(9):2795-2805.
[240]Jianquan Lua, Daniel W.C. Ho, Stabilization of complex dynamical networks with noise disturbance under performance constraint [J], Nonlinear Anal.:Real World Appl.,2011,12(4):1974-1984.
[241]W. Zhong, J. D. Stefanovski, G. M. Dimirovski, J. Zhao, Decentralized control and synchronization of time-vary ing complex dynamical network [J], Kybernetika,2009,45(1):151-167.