基于生物网络的智能控制系统及其应用
|
摘要
在现代工业过程控制中，随着要求产品质量越来越高，对生产过程的控制效果提出了更高的要求。同时，在现代复杂信息环境下，出现了越来越多的复杂控制复杂控制系统。因此，需要开发研究智能化程度更高、实用性更强的智能控制算法。本文基于神经内分泌免疫生物网络系统的多种生物调节机制，对相关智能控制算法进行了研究。
首先，对人工神经网络、人工免疫系统和人工内分泌系统等人工生物智能理论及各种智能控制技术发展进行了综述，指出了目前发展存在的问题以及将来的发展方向。接着，对神经系统、内分泌系统和免疫系统的生理基础及其调控机理或网络模型进行了介绍，为本文各种智能控制算法的研究设计奠定了生物理论基础。然后，基于NEI的调节机制，立足于解决过程控制中的实际问题，结合传统控制理论技术，从智能控制、学习控制、解耦控制、优化控制和网络控制等方面，对相关的智能控制技术进行了深入研究。
在智能控制方面，首先基于下丘脑—垂体—睾丸素内分泌调节回路模型，设计了一种双层结构控制器。该控制器包括一级控制单元和二级控制单元两部分。一级控制单元根据控制偏差的大小，动态调整二级控制单元的设定值输入，从而能够迅速、稳定地消除控制偏差。然后，基于内分泌系统超短反馈调节机制，设计了一种超短反馈智能控制器。该控制器的传统控制单元的输出信号反馈给超短反馈处理单元，然后超短反馈处理单元按照激素调节分泌规律进行处理，处理后的信号与原传统控制单元输出信号叠加，从而构成一种非线性控制算法并提高控制效果。最后，通过仿真实验分别对两种智能控制器的控制性能进行了验证，结果表明其控制性能均优于传统PID控制器。
在学习控制方面，基于免疫系统的初次—再次应答机制，设计了一种新颖的增强学习智能控制器(RLIC)。该RLIC具有学习、记忆、和进化能力，能够在消除控制偏差的过程中自动地形成控制抗体。当控制偏差再次出现时，RLIC能够结合传统控制算法，快速、稳定地消除控制偏差。控制偏差消除后，新的控制抗体即形成。这样随着控制器消除偏差次数的增多，其学习能力和响应速度变得越来越强。仿真实验表明，该学习控制算法不但优于传统控制算法，也优于传统的Q增强学习控制算法。
在解耦控制方面，基于内分泌生长激素双向调节原理，设计了一种仿生双向解耦控制器和一种逆控制解耦控制器，并分别给出了相应的解耦算法。这两种解耦控制器分别根据不同的解耦算法，通过协调控制相应的执行器，从而消除不同控制回路之间的耦合影响。与其它解耦控制技术相比，这两种解耦控制算法比较实际，且更容易实现。通过仿真实验，分别将两种解耦控制算法与传统控制算法进行比较，实验结果表明智能解耦控制算法的解耦效果优于传统控制算法。
在优化控制方面，基于内分泌激素调节规律，提出了一种自适应遗传算法(HGA)，该算法的收敛速度、搜索精度均优于标准遗传算法。并在此基础上，基于不同的神经内分泌免疫系统调节机理，先后设计了两种非线性优化智能控制器。第一种是基于NEI系统的整体调节机制的非线性优化控制器(NOIC)。根据免疫提呈机制，NOIC的提呈单元首先对实时控制偏差进行预处理，然后抗体控制单元通过调整抗体控制实体的数目来消除控制偏差。主控单元调节或协调提呈单元和控制抗体单元的控制作用，优化单元和辨识单元优化实时控制参数，从而提高NOIC的控制性能。第二种是基于肾上腺激素调节机制的智能优化控制器(ALIC)。根据实时控制偏差和激素调节规律，ALIC的主控制单元动态调整副控制单元的控制参数；利用HGA，优化单元和辨识单元可以优化主控制单元和副控制单元的控制参数，从而提高控制性能。通过仿真实验表明，这两种智能优化控制器比传统优化控制器均具有更好的控制性能。
在网络控制方面，首先基于神经、内分泌和免疫三大系统整体调节机制，提出了一种新颖的分布式网络控制体系架构。然后提出了基于HGA的远程网络辨识算法和远程网络优化控制算法。最后利用一种6自由度微型操平台模型，对提出的网络辨识和优化控制算法进行了验证。
最后，对全文研究内容进行了总结，指出研究工作中存在的不足，明确了下一步的研究方向。
During the process control of modern industry, product quality is required higher and higher, which demands control performance with more efficiency. And, more intelligent and practical control algorithms are required by more and more complex control systems at the environment of the modern involuted information. Based on some bio-regulation mechanisms of the neuroendocrine-immune system, some intelligent control algorithms are studied in this thesis.
First, we investigate the development of artificial bio-intelligent technologies, including artificial neural network, artificial immune system, artificial endocrine system, and the others intelligent control technologies. And their difficulties and further developments are summarized. Furthermore, some relative physiological theories and modulation mechanisms or models of neural system, endocrine system, and immune system, are introduced briefly. That provides the bio-base for the intelligent control algorithms studied in this thesis. Then, based on some bio-regulation mechanisms of the neuroendocrine-immune system, some intelligent control algorithms including intelligent control, learning control, decoupling control, optimized control and networked control, are studied respectively.
For intelligent control, a two-level controller is first presented based on the hypothalamo-pituitary-adrenal model. The two-level controller includes the master control unit and the secondary one. The master control unit can adjust dynamically setpoint of the secondary one according to the real-time control error. Consequently, the controller can eliminate control error quickly and stably. Next, an ultrashort feedback intelligent controller is presented based on the ultrashort feedback mechanism of endocrine system. The output of the conventional control unit (CCU) is first fed back to the ultralshort-feedback unit (UFU), where the output of CCU is processed according to the hormone regulation law. Then the output of UFU is added to the output of CCU. Thus a nonlinear control algorithm is built. Consequently, the control performance is improved. Finally, the control performances of both controllers are examined via simulation experiments, whose results demonstrate the control performance and adaptation of both controllers are better than that of the conventional PID controller.
For learning control, a novel reinforcement learning intelligent controller (RLIC) is presented based on the primary-secondary response mechanism. The RLIC has the abilities of learning, memory, and evolution, and can learn and produce the control antibodies (CABs) automatically during the period of eliminating the control error. When the control error appears again, the RLIC can eliminate it rapidly and stably, combined with the conventional control algorithm. After the control error is eliminated, a new CAB is produced and stored. Repeating the above process, the RLIC's learning ability and response rate become stronger and stronger. Consequently, the control performance of the RLIC can be improved. Simulation results demonstrate that response ability and stability of the RLIC are better than those of the conventional PID controller, and also better than the Q-reinforcement learning control.
For decoupling control, a bio-imitated decoupling controller and an inverse decoupling controller are presented respectively, based on the bi-regulation mechanism of the growth hormone (GH) in endocrine system. And the corresponding decoupling control algorithms are also provided. Both decoupling controllers can eliminate the coupling influence between different control loops via adjusting actuators harmoniously, according to their related decoupling algorithms. Compared with the others decoupling control technologies, both the decoupling controller are more practical and implemented more easily. The results of simulation indicate that the schemes of both decoupling controller can completely eliminate the coupling influence and show better control performance.
For optimized control, a novel adaptive genetic algorithm (HGA) is first presented based on the regulation law of hormone in endocrine system. The convergence rate and search precision of HGA are better than that of the standard genetic algorithm (GA). Then, two optimized controllers are presented respectively according to HGA and based on the different modulation mechanism of neuroendocrine-immune system. The first one is a novel nonlinear optimized intelligent controller (NOIC) based on the modulation mechanism of neuroendocrine-immune system. Also, a method to optimize and adjust the control parameters dynamically is provided, as thus the control performance is improved. According to the presentation mechanism of immune system, the presentation unit (PU) first pretreats real-time control error dynamically, and then the antibody control unit (ACU) can regulate the number of antibody control entities (ACEs) to eliminate control error. The main control unit can regulate the control action of PU and ACU. Furthermore the optimum unit (PU) and identification unit (IU) can optimized the real-time control parameters. Thus, the control performance of NOIC is improved. The second one is an intelligent optimized controller based on the regulation mechanism of adrenalin (ALIC) in endocrine system. The method to optimize and adjust the control parameters dynamically is also provided, and thus the control performance of ALIC is improved. The simulation results demonstrate that the control performances of the NOIC and ALIC are better than that of the conventional PID controller.
For networked control, a novel architecture of distributed networked control system is first presented. And then the model identification and optimized control methods via remote network are presented respectively according to HGA. Finally, the network identification and optimized algorithms are applied in the micro-motion platform mechanism with 6 DOF.
At last, a summary of the thesis is made, and the deficiency in the project and the further development are narrated respectively.
首先，对人工神经网络、人工免疫系统和人工内分泌系统等人工生物智能理论及各种智能控制技术发展进行了综述，指出了目前发展存在的问题以及将来的发展方向。接着，对神经系统、内分泌系统和免疫系统的生理基础及其调控机理或网络模型进行了介绍，为本文各种智能控制算法的研究设计奠定了生物理论基础。然后，基于NEI的调节机制，立足于解决过程控制中的实际问题，结合传统控制理论技术，从智能控制、学习控制、解耦控制、优化控制和网络控制等方面，对相关的智能控制技术进行了深入研究。
在智能控制方面，首先基于下丘脑—垂体—睾丸素内分泌调节回路模型，设计了一种双层结构控制器。该控制器包括一级控制单元和二级控制单元两部分。一级控制单元根据控制偏差的大小，动态调整二级控制单元的设定值输入，从而能够迅速、稳定地消除控制偏差。然后，基于内分泌系统超短反馈调节机制，设计了一种超短反馈智能控制器。该控制器的传统控制单元的输出信号反馈给超短反馈处理单元，然后超短反馈处理单元按照激素调节分泌规律进行处理，处理后的信号与原传统控制单元输出信号叠加，从而构成一种非线性控制算法并提高控制效果。最后，通过仿真实验分别对两种智能控制器的控制性能进行了验证，结果表明其控制性能均优于传统PID控制器。
在学习控制方面，基于免疫系统的初次—再次应答机制，设计了一种新颖的增强学习智能控制器(RLIC)。该RLIC具有学习、记忆、和进化能力，能够在消除控制偏差的过程中自动地形成控制抗体。当控制偏差再次出现时，RLIC能够结合传统控制算法，快速、稳定地消除控制偏差。控制偏差消除后，新的控制抗体即形成。这样随着控制器消除偏差次数的增多，其学习能力和响应速度变得越来越强。仿真实验表明，该学习控制算法不但优于传统控制算法，也优于传统的Q增强学习控制算法。
在解耦控制方面，基于内分泌生长激素双向调节原理，设计了一种仿生双向解耦控制器和一种逆控制解耦控制器，并分别给出了相应的解耦算法。这两种解耦控制器分别根据不同的解耦算法，通过协调控制相应的执行器，从而消除不同控制回路之间的耦合影响。与其它解耦控制技术相比，这两种解耦控制算法比较实际，且更容易实现。通过仿真实验，分别将两种解耦控制算法与传统控制算法进行比较，实验结果表明智能解耦控制算法的解耦效果优于传统控制算法。
在优化控制方面，基于内分泌激素调节规律，提出了一种自适应遗传算法(HGA)，该算法的收敛速度、搜索精度均优于标准遗传算法。并在此基础上，基于不同的神经内分泌免疫系统调节机理，先后设计了两种非线性优化智能控制器。第一种是基于NEI系统的整体调节机制的非线性优化控制器(NOIC)。根据免疫提呈机制，NOIC的提呈单元首先对实时控制偏差进行预处理，然后抗体控制单元通过调整抗体控制实体的数目来消除控制偏差。主控单元调节或协调提呈单元和控制抗体单元的控制作用，优化单元和辨识单元优化实时控制参数，从而提高NOIC的控制性能。第二种是基于肾上腺激素调节机制的智能优化控制器(ALIC)。根据实时控制偏差和激素调节规律，ALIC的主控制单元动态调整副控制单元的控制参数；利用HGA，优化单元和辨识单元可以优化主控制单元和副控制单元的控制参数，从而提高控制性能。通过仿真实验表明，这两种智能优化控制器比传统优化控制器均具有更好的控制性能。
在网络控制方面，首先基于神经、内分泌和免疫三大系统整体调节机制，提出了一种新颖的分布式网络控制体系架构。然后提出了基于HGA的远程网络辨识算法和远程网络优化控制算法。最后利用一种6自由度微型操平台模型，对提出的网络辨识和优化控制算法进行了验证。
最后，对全文研究内容进行了总结，指出研究工作中存在的不足，明确了下一步的研究方向。
During the process control of modern industry, product quality is required higher and higher, which demands control performance with more efficiency. And, more intelligent and practical control algorithms are required by more and more complex control systems at the environment of the modern involuted information. Based on some bio-regulation mechanisms of the neuroendocrine-immune system, some intelligent control algorithms are studied in this thesis.
First, we investigate the development of artificial bio-intelligent technologies, including artificial neural network, artificial immune system, artificial endocrine system, and the others intelligent control technologies. And their difficulties and further developments are summarized. Furthermore, some relative physiological theories and modulation mechanisms or models of neural system, endocrine system, and immune system, are introduced briefly. That provides the bio-base for the intelligent control algorithms studied in this thesis. Then, based on some bio-regulation mechanisms of the neuroendocrine-immune system, some intelligent control algorithms including intelligent control, learning control, decoupling control, optimized control and networked control, are studied respectively.
For intelligent control, a two-level controller is first presented based on the hypothalamo-pituitary-adrenal model. The two-level controller includes the master control unit and the secondary one. The master control unit can adjust dynamically setpoint of the secondary one according to the real-time control error. Consequently, the controller can eliminate control error quickly and stably. Next, an ultrashort feedback intelligent controller is presented based on the ultrashort feedback mechanism of endocrine system. The output of the conventional control unit (CCU) is first fed back to the ultralshort-feedback unit (UFU), where the output of CCU is processed according to the hormone regulation law. Then the output of UFU is added to the output of CCU. Thus a nonlinear control algorithm is built. Consequently, the control performance is improved. Finally, the control performances of both controllers are examined via simulation experiments, whose results demonstrate the control performance and adaptation of both controllers are better than that of the conventional PID controller.
For learning control, a novel reinforcement learning intelligent controller (RLIC) is presented based on the primary-secondary response mechanism. The RLIC has the abilities of learning, memory, and evolution, and can learn and produce the control antibodies (CABs) automatically during the period of eliminating the control error. When the control error appears again, the RLIC can eliminate it rapidly and stably, combined with the conventional control algorithm. After the control error is eliminated, a new CAB is produced and stored. Repeating the above process, the RLIC's learning ability and response rate become stronger and stronger. Consequently, the control performance of the RLIC can be improved. Simulation results demonstrate that response ability and stability of the RLIC are better than those of the conventional PID controller, and also better than the Q-reinforcement learning control.
For decoupling control, a bio-imitated decoupling controller and an inverse decoupling controller are presented respectively, based on the bi-regulation mechanism of the growth hormone (GH) in endocrine system. And the corresponding decoupling control algorithms are also provided. Both decoupling controllers can eliminate the coupling influence between different control loops via adjusting actuators harmoniously, according to their related decoupling algorithms. Compared with the others decoupling control technologies, both the decoupling controller are more practical and implemented more easily. The results of simulation indicate that the schemes of both decoupling controller can completely eliminate the coupling influence and show better control performance.
For optimized control, a novel adaptive genetic algorithm (HGA) is first presented based on the regulation law of hormone in endocrine system. The convergence rate and search precision of HGA are better than that of the standard genetic algorithm (GA). Then, two optimized controllers are presented respectively according to HGA and based on the different modulation mechanism of neuroendocrine-immune system. The first one is a novel nonlinear optimized intelligent controller (NOIC) based on the modulation mechanism of neuroendocrine-immune system. Also, a method to optimize and adjust the control parameters dynamically is provided, as thus the control performance is improved. According to the presentation mechanism of immune system, the presentation unit (PU) first pretreats real-time control error dynamically, and then the antibody control unit (ACU) can regulate the number of antibody control entities (ACEs) to eliminate control error. The main control unit can regulate the control action of PU and ACU. Furthermore the optimum unit (PU) and identification unit (IU) can optimized the real-time control parameters. Thus, the control performance of NOIC is improved. The second one is an intelligent optimized controller based on the regulation mechanism of adrenalin (ALIC) in endocrine system. The method to optimize and adjust the control parameters dynamically is also provided, and thus the control performance of ALIC is improved. The simulation results demonstrate that the control performances of the NOIC and ALIC are better than that of the conventional PID controller.
For networked control, a novel architecture of distributed networked control system is first presented. And then the model identification and optimized control methods via remote network are presented respectively according to HGA. Finally, the network identification and optimized algorithms are applied in the micro-motion platform mechanism with 6 DOF.
At last, a summary of the thesis is made, and the deficiency in the project and the further development are narrated respectively.
引文
[1] 丁永生．计算智能——理论、技术与应用[M]．北京：科学技术出版社，2004，8．
[2] 陈宗海．智能自动化技术的现状与发展趋势[J]．自动化博览，2001，18(2)：4-7．
[3] G. Stephanopoulos and C. Han. Intelligent systems in process engineering: a review[J]. Computers & Chemical Engineering, 1996, 20(6-7): 743-791.
[4] M. Clive and W. Winston. Development of an intelligent controller for power generators [J]. Journal of Physics: Conference Series, 2005, 15: 306-310.
[5] M. Lind. Status and challenges of intelligent plant control [J]. Annual Reviews in Control, 1996, 20: 23-41.
[6] 戴汝为，王玉．关于智能系统的综合集成[J]．科学通报，1993，38(14)：1249-1256．
[7] 盛万兴，戴汝为．关于智能控制[J]．电子学报，1999，27(5)：86-89．
[8] M. A. Thomas. Intelligent "control" applications in the process industries [J]. Annual Reviews in Control, 2002, 26(1): 75-86.
[9] 蔡自兴．智能控制四元结构的研究[J]．高技术通讯，1996．6(7)：19-23．
[10] L. A. Zadeh. Fuzzy sets [J]. Information and Control, 1965, (8): 330-353.
[11] K. S. Narendra. Intelligent control [J]. Control Systems Magazine, 1991, 11(1): 39-40.
[12] 刘向杰，周孝信，柴天佑．模糊控制研究的现状与新发展[J]．信息与控制，1999，28(4)：283-292．
[13] W. J. Wang and B. Y. Tang. A fuzzy adaptive method for intelligent control [J]. Expert Systems with Applications, 1999, 16(1): 43-48.
[14] E. H. Mamdani and B. R. Gains. Fuzzy reasoning and its applications [M]. London: Academic, 1981.
[15] R. D. Sarma and N. Ajamai. Fuzzy nets and their application [J]. Fuzzy Sets and Systems, 1992, 45: 41-51.
[16] K. S. Fu. Learning control system and intelligent control system: an intersection of artificial intelligence and automatic control [J]. IEEE Trans. on AC, 1971, 16(1): 70-72.
[17] S. I. Gallant. Network learning and expert systems [J]. Communications of the ACM, 1988, 31(2): 152-169.
[18] 张再兴，孙增沂．关于专家控制[J]．信息与控制，1995，24(3)：167-172．
[19] 邱东强．神经网络控制的现状与展望[J]．自动化与仪器仪表，2001，(5)：1-7．
[20] D. de Ridder, R. P. W. Duin, P. W. Verbeek and L. J. van Vliet. The applicability of neural networks to non-linear image processing [J]. Pattern Analysis & Applications. 1999, (2): 111-128.
[21] 商琳，王金根，姚望舒，陈世福．一种基于多进化神经网络的分类方法[J]．软件学报，2005，16(9)：1577-1583．
[22] S. -Y. Yun, S. Namkoong, J. -H. Rho, etc. A performance evaluation of neural network models in traffic volume forecasting [J]. Mathl. Comput. Modeling, 1998, 27(9-11): 293-310.
[23] C. T. Lin and C. S. G. Lee. Neural: neural-network-based fuzzy logic control and decision system [J]. IEEE Trans. on Computer, 1991, 40(12): 1320-1336.
[24] F. Mauricio and G. Fernando. Design of fuzzy system using neurofuzzy networks[J]. IEEE Trans. on Neural Networks. 1999, 10(4): 815-827.
[25] C. T. Chen and S. T. Peng. Intelligent process control using neural fuzzy techniques [J]. Journal of Process Control, 1999, 9(6): 493-503.
[26] 刘曙光，费佩燕，侯志敏．遗传算法的进展与展望[J]．现代电子技术，2000，(6)：83-87．
[27] P. J. Fleming and R. C. Purshouse. Evolutionary algorithms in control systems engineering: a survey [J]. Control Engineering Practice, 2002, 10(11): 1223-1241.
[28] H. S. Yasmin. On Genetic Algorithms and their Applications [J]. Handbook of Statistics, 2005, 24,: 359-390.
[29] 段玉倩，贺家李．遗传算法及其改进[J]．电力系统及其自动化学报，1998，10(1)：39-52．
[30] E. M Norberto, F. Takeshi, Tomonori, et al. A study on the discovery of relevant fuzzy rules using pseudo-bacterial genetic algorithms [J]. IEEE Trans. on Industry Applications. 1999, 46(6): 1080-1089.
[31] X. Ning and L. Lathar. A new genetic based approach to fuzzy controller design and its application [A]. Proceedings of the 1998 IEEE International Conference on Control Applications [C]. Trieste, Italy, Sept. 1-4, 1998, 937-941.
[32] R. M. Dimeo and K. Y. Lee. Genetics-based control of a MIMO boiler turbine plant [A]. Proceedings of the 33rd Conference on Decision and Control [C]. Lake Buena Vista, Florida, Dec. 14-16, 1994, 3512-3517.
[33] S. S. Mei, Z. Huang and K. L. Fang. A neural network controller based on genetic algorithms [A]. Proceedings of IEEE International Conference on Intelligent Processing Systems [C], Beijing, China, Oct. 28-31, 1997, 1624-1628.
[34] Z. J. Liu and M. Sugisaka. A genetic algorithm approach used to generate the neural network structures [A]. Proceedings of the 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems [C], 1999, 763-768.
[35] B. Khaled and T. Faouzi. Genetic algorithm for the design of a class fuzzy controller: an alternative approach [J]. IEEE Trans. on Fuzzy Systems. 2000, 8(4): 398-405.
[36] M. Srinivas. Adaptive probabilities of crossover and mutation in genetic algorithms [J]. IEEE Trans. on Systems, Man and Cybernetics, 1994, 24(4): 656-667.
[37] 丁永生，任立红．人工免疫系统：理论与应用[J]．模式识别与人工智能，2000，13(1)：52-59．
[38] L. -C. Jiao and L. Wang. Novel genetic algorithm based on immunity [J]. IEEE Trans on Systems, Man and Cybernetics-Part A: Systems and Humans, 2000, 30(5): 552-561.
[39] 肖人彬，王磊．人工免疫系统：原理、模型、分析及展望[J]．计算机学报，2002，25(12)：1281-1293．
[40] L. N. De Castro and F. J. Vov Zuben. An immuneological approach to initialize centers of radial basis function neural networks [A]. Proceedings of V Brazilian Confon Neural Networks[C]. Rio de Janeiro, Brazil, 2001, 79-84.
[41] J. G. Zheng, L. Wang and L. C. Jiao. An immune algorithm for generalized rule induction [A]. Proceedings of CIE Int Con of Radar Proc [C]. Beijing, China, 2001, 1023-1026.
[42] T. Gong and Z. X. Cai. Mobile immune-robot model [A]. Proceeding of IEEE Int Conf on Robotics, Intelligent Systems and Signal Proc[C]. Changsha, China, Oct. 8-13, 2003, 1091-1096.
[43] J. Timmis and M. Neal. Artificial homeostasis: integrating biologically inspired computing [EB/OL], www.cs.kent.ac.uk/pubs/2003/1586/content.pdf, Apr. 15, 2004.
[44] P. Vargas, R. Moioli, LN De Castro, et al. Artificial homeostasis: a novel approach [A], Proceedings of ECAL 2005 [C], LNAI, 2005, 3630, 754-763.
[45] 黄国锐．人工内分泌模型及其应用研究[D]．中国科学与技术大学博士学位论文，2003，6．
[46] H. Besedovsky and E. Sorkin. Network of immune-neuroendocrine interactions [J]. Clinical and Experimental Immunology, 1977, 27: 1-12.
[47] G. Dan and S. B. Lall. Neuroendocrine modulation of immune system [J]. Indian Journal of Pharmacology, 1998, 30: 129-140.
[48] B. Brazzini, I. Ghersetich, J. Hercogova and T. Lotti. The neuro-immuno-cutaneousendocrine network: relationship between mind and skin [J]. Dermatologic Therapy, 2003, 16: 123-131.
[49] W. Savino and M. Dardenne. Neuroendocrine control of thymus physiology [J]. Endocrine Reviews, 2000, 21(4): 412-443.
[50] D. M. Keenan and J. D. Veldhuis. A biomathematical model of time-delayed feedback in the human male hypothalamic-pituitary-leydig cell axis [J]. American Journal of Physiology, 1998, 275: 157-176.
[51] L. S. Farhy. Modeling of oscillations of endocrine networks with feedback [J]. Methods Enzymol, 2004, 384: 54-81.
[52] P. Vargas, M. R. L. oiolil, N. D. Castro, et al. Artificial homeostatic system: a novel approach [A]. Proceeding of ECAL 2005 [C], LNAI, 2005, 3630: 754-764.
[53] 马勇，许晓鸣，张卫东．一种改进的基于再励学习算法的模糊神经BOXES控制系统[J]．模糊系统与数学，2000，14(1)：78-83．
[54] P. E. Macrossan, H. A. Abbass, K. Mengersen, et al. Bayesian neural network learning for prediction in the Australian dairy industry [J]. Lecture Notes in Computer Science, 1999, 1642: 395-406.
[55] K. S. Fu and M. Waltz. A heuristic approach to reinforcement learning control system [J]. IEEE Trans. on AC, 1965, 10(4): 390-398.
[56] H. O. Besedovsky and E. Sorkin. Network of immune-neuroendocrine interaction[J]. Clinical and Experimental Immunology, 1977, 27: 1-12.
[57] 谢蜀生．神经-内分泌-免疫网络[J]．科技导报，1994，(8)：11-12，64．
[58] 胡格，穆祥，段慧勤，杨佐君，高立云．免疫、神经和内分泌系统间的关系[J]，动物医学进展，2003，24(1)：5-7．
[59] 王玢，左明雪．人体及动物生理学[M]．北京：高等教育出版社，2001，7．
[60] I. M. Kvetnoy. Neuroimmunoendocrinology: Where is the field for study? [J]. Neuroendocrinology Letters, 2002, 23: 119-120.
[61] I. G. Akmaev. Problems and prospects in development of neuroimmunoendocrinology [J]. Problemy Endokrinologii -Moskva- Meditsina, 1999, 45(5): 3-7.
[62] 关新民．医学神经生物学纲要[M]．北京：科学出版社，2003：267-285．
[63] R. P. Lippmann. An introduction to computing with neural nets [J], IEEE ASSP Magazine, 1987, 4(2): 4-22.
[64] 朱大奇．人工神经网络研究现状及其展望[J]，江南大学学报(自然科学版)，2004，3(1)：103-110．
[65] 吴宏岐，张军利，周妮娜．基于神经网络的智能控制技术及应用[J]．信息技术，2004，28(1)：1-3．
[66] M. Dornier, B. Heyd and M. Danzart. Evaluation of the simplex method for training simple multilayer neural networks [J]. Neural Comput & Applic, 1998, (7): 107-114.
[67] M. Dornier, B. Heyd and M. Danzart. Evaluation of the simplex method for training simple multilayer neural networks [J]. Neural Comput & Applic, 1998, (7): 107-114.
[68] Y. -X. Huang, Y. Wang, W. G. Zhou, et al. A fuzzy neural network system based on generalized class cover and particle swarm optimization [J]. Lecture Notes in Computer Science, 2005, 3645: 119-128.
[69] M. Inoue and A. Nagayoshi. A chaos neural-computer [J]. Physical Letter A, 1991, 158(8): 373-376.
[70] M. Inoue and K. Nakamoto. Epilepsy in a chaos neural-computer model, chaos in biology and medicine [J]. SPLE, 1993, 236: 77-84.
[71] D. Ventura and R. Martinez. Quantum associated memory [J]. Information Sciences, 2000, 124: 147-148.
[72] D. Ventura. Quantum computing and neural information processing [J]. Information Sciences, 2000, 128: 273-296.
[73] A. D. Mcaulay and M. C. Wang. Optical hetero associative memory using spatial light rebroadcasters [J]. Applied Optical, 1990, 29(14): 2067-2073.
[74] I. Kucuk and N. Derebasi. Prediction of power losses in transformer cores using feed forward neural network and genetic algorithm [J]. Measurement, 2006, 39(7): 605-611.
[75] A. Blanco, M. Delgado and M. C. Pegalajar. A genetic algorithm to obtain the optimal recurrent neural network [J]. International Journal of Approximate Reasoning, 2000, 23(1): 67-83.
[76] D. D. Ridder, R. P. W. Duin, P. W. Verbeek and L. J. van Vliet. The applicability of neural networks to non-linear image processing [J]. Pattern Analysis & Applications, 1999, (2): 111-128.
[77] Jasmina Arifovic and Ramazan Gencay. Using genetic algorithms to select architecture of a feedforward artificial neural network [J]. Physica A: Statistical Mechanics and its Applications, 2001, 289(3-4): 574-594.
[78] G. -C. Chen and J. S. Yu. Particle swarm optimization neural network and its application in soft-sensing modeling [J]. Lecture Notes in Computer Science, 2005, 3611, 610-617.
[79] B. Chaudhuri and J. M. Modak. Optimization of fed-batch bioreactor using neural network model [J]. Bioprocess Engineering, 1998, 19: 71-79.
[80] J. L. H. John. Categorization of fetal heart rate patterns using neural networks [J]. Journal of Medical Systems, 2001, 25(4): 269-276.
[81] M. Perus and P. Ecimovic. Memory and pattern recognition in associative neural networks [J]. International Journal of Applied Science and Computation, 1998, (4): 283-310.
[82] S. Ferrari and R, F. Stengel. Smooth function approximation using neural networks [J]. IEEE Transactions on Neural Networks, 2005, 16 (1): 24-38.
[83] 文绍纯．基于遗传算法的人工神经网络的应用综述[J]．自动化与仪器仪表，2001，(6)：1-4．
[84] 陆德源，马宝骊．现代免疫学[M]．上海：上海科技教育出版社，1998，12．
[85] N. D. C. Leandro and J. V. Z. Femando. Learning and optimization using the clonal selection principle [J]. IEEE Trans on Evolutionary Computation, 2002, 6(3): 306-313.
[86] A. S. Perelson. Immune network theory [J]. Immunological Review, 1986, (10): 5-36.
[87] A. Ishiguro, S. Ichhikawa and Y. Uchikawa. A gait acquisition of 6-legged walking robot using immune networks [A], Proceeding of IROS'94 [C], 1994, 2: 1034-1041.
[88] A. Ishiguro, Y. Watanabe, S. Ichikawa, et al. Gait control of hexapod walking robots using mutual-coupled immune network [J]. Advanced Robotics, 1996, 10(2): 179-195.
[89] N. K. Jeme. Towards a network theory of the immune System [J]. Ann Immunol, 1974, 125C: 373-389.
[90] J. D. Farmer, N. H. Packard and A. S. Prelson. The immune system, adaptation, and machine learning [J]. Physical D, 1986, 22: 187-204.
[91] S. A. Hofmeyr and S. Forrest. Immunity by design: an artificial immune system [A]. Proceeding of GECCO'99 [C], 1999, 1289-1296.
[92] R. N. Germain. MHC-associated antigen processing, presentation, and recognition, adolescence, maturity and beyond [J]. The Immunologist, 1995, (6): 185-190.
[93] J. Timmis and M. Neal. A resource limited artificial system for data analysis [J]. Knowledge-Based-Systems, 2001, 14: 121-130.
[94] Z. Tang, T. Yamaguchi, K. Tashima, et al. Multiple-valued immune network model and its simulation [A]. Proceeding of 27th Int Symposium on Multiple-valued Logic [C], Autigonish, Canada, 1997, 233-238.
[95] D. Castro and J. Fernando. An evolutionary immune network for data clustering [A]. Proceeding of the IEEE. SBRN [C], 2000, 84-89.
[96] F. T. Vertosick and R. H. Kelly. Immune network theory: a role for paralled distributed processing [J]. Immunlogy, 1989, 66: 1-7.
[97] J. S. Chun, M. K. Kim, H. K. Jung, et al. Shape optimization of electromagnetic device using immune algorithm [J]. IEEE Trans. on Magnetics, 1997, 33(2): 1876-1879.
[98] Y. Ishida and N. Adachi. Active noise control by an immune algorithm: adaptation immune system as an evolution [A]. Proceeding of ICEC 96 [C], 1996, 150-153.
[99] P. Dhaeseleer, S. Forrest and P. Helman. An immunological approach to change detection algorithms: Analysis and implications [A]. Proceeding of IEEE Symposium on Security and Privacy [C], Las Alamitos, CA, USA, 1996, 110-119.
[100] L. N. De Castro and F. J. VonZuben. Clonal selection algorithm with engineering applications [A]. Proceeding of GECCO'00 [C], LasVegas, Nevada, USA, 2000, 36-37.
[101] Y. -S. Ding and L. -H. Ren. Fuzzy self-tuning immune feedback controller for tissue hyperthermia [A]. Proceeding of Fuzzy 1EEE 2000: The Ninth IEEE International Conference [C], May 7-10, 2000, 1: 534-538.
[102] B. Liu and Y. -S. Ding. An intelligent controller based on primary-secondary responding mechanism of immune system [A]. Proceeding of the 7th International Conference on Computational Intelligence and Natural Computing (CIN-3) [C], 2005, 467-470.
[103] D. H. Kim. PID controller tuning of a boiler control system using immune algorithm typed neural network [A]. Proceeding of Computational Science - ICCS 2004: the 4th International Conference [C], Krakow, Poland, Jun. 6-9, 2004, 695-698.
[104] D. H. Kim and J. I. Park. Intelligent PID control by immune algorithms based fuzzy rule auto-tuning [J]. Lecture Notes in Computer Science, 2003, 2715: 474-482.
[105] G. -C. Luh and C. -H. Chueh. Multi-modal topological optimization of structure using immune algorithm [J]. Computer Methods in Applied Mechanics and Engineering, 2004, 193(36-38): 4035-4055.
[106] 谢启文．现代神经内分泌学[M]．上海：上海医科大学出版社，1999．
[107] D. M. Keenan, J. Licinio and J. D. Veldhuis. A feedback-controlled ensemble model of the stress-responsive hypothalamo-pituitary-adrenal axis [J]. PNAS, 2001, 98(7): 4028-4033.
[108] L. S. Farhy, M. Straume, et al. A construct of interactive control of the GH axis in the male [J]. Am J Physiol Regulatory Integration Comp Physiol, 2001, 281: 38-51.
[109] L. S. Farhy, Modeling of oscillations of endocrine networks with feedback [EB/OL]. http://www.people.virginia.edu/～lsf8n/Educational.pdf, May 14, 2004.
[110] E. A. Kraitz. Hormonal control for behavior: amines and the biasing of behavioral output in lobsters [J]. Science, 1988, 241: 1775-1781.
[111] R. A. Brooks. Integrated systems based on behaviours [J]. SIGART Bulletion, 1991, 2(4): 46-50.
[112] D. M. Keenan, J. Licinio and J. D. Veldhuis. A feedback-controlled ensemble model of the stress-responsive hypothalamo-pituitary-adrenal axis [J]. PNAS, 2001, 98(7): 4028-4033.
[113] B. Liu, K. R. Hao and Y. S. Ding. A nonlinear optimized controller based on modulation of testosterone [A]. Proceeding of The Third International Conference on Computational Intelligence, Robotics and Autonomous Systems (CIRAS 2005) [C], Singapore, Dec. 14-16, 2005.
[114] B. Liu and Y. S. Ding. A decoupling control based on the bi-regulation principle of growth hormone [A]. Proceeding of 2005 ICSC Congress on Computational Intelligence: Methods & Application 2005 (CIMA'05) [C], Istanbul, Turkey, Dec. 15-17, 2005.
[115] B. Liu, Y. S. Ding and J. H. Wang, An intelligent controller inspired from neuroendocrine-immune system [A]. Proceeding of 2006 International Conference on Intelligent Systems & Knowledge Engineering (ISKE2006) [C], Shanghai, China, Apr. 6-7, 2006.
[116] 苏小红，杨博，王亚东．基于进化稳定策略的遗传算法[J]．软件学报，2003，14(11)：1863-1868．
[117] 刘福才，潘江华，路平立，裴润．一种改进的变焦遗传算法[J]．信息与控制，2004，33(1)：82-84．
[118] D. W. Wang and S. C. Fang. A genetic-based approach for aggregated production planning in a fuzzy environment [J]. IEEE Trans. on Systems, Man, and Cybernetics-Part A: Systems and Humans, 1997, 27(5): 636-645.
[119] C. Co Chen and C. -C. Wang. Self-generating rule-mapping fuzzy controller design using a genetic algorithm [A]. Proceeding of IEE Proc. -Control Theory Apple [C], 2002, 149(2): 143-148.
[120] H. K. Lam, S. H. Ling, F. H. F. Leung, et al. Optimal and stable fuzzy controllers for nonlinear systems subject to parameter uncertainties using genetic algorithms [J]. IEEE Transactions on Industrial Electronics, 2004, 51(1): 172-182.
[121] Y. S. Zhou and L. Y. Lai. Optimal design for fuzzy controllers by genetic algorithms [J]. EEE Transaction on Industry Applications, 2000, 36(1): 93-97.
[122] 李建华，王孙安．最优家族遗传算法[J]．西安交通大学学报，2004，38(1)：77-80．
[123] 谢晓锋，张文俊，杨之廉．一种防止浮点遗传算法早熟收敛的父代选择策略[J]．控制与决策，2002，17(5)：625-634．
[124] J. J. Grefenstete. Optimization of control parameters for genetic algorithms [J]. IEEE Trans. on systems, Man and Cybernetics, 1986, 16(1): 122-128.
[125] Y. -M. Li, X. -P. Liu and Z. -Y. Peng. A fuzzy method approach for identification of joint parameters of mechanical structures [J]. Journal of Mechanical Engineering, 2002, 53(5): 280-288.
[126] M. R. M. Rizk, El-Arabaey and H. S. Khaddam. An algorithm for optimum stability region of fuzzy control systems using genetic algorithms [A]. Proceedings of the American Control Conference [C], Arlington, Jun. 25-27, 2001, 192-197.
[127] S. Nishida, R. Palmer, C. Slaughterbeck and S. Walker. An expert system for control curve evaluation during drought [A]. Proceeding of the 17th Annual National Conference, Water Resources Planning and Management Division [C], ASCE, Ft Worth, Texas, 1990, 294-297.
[128] M. Livchitz, A. Abershitz, U. Soudak and A. Kande. Development of an automated fuzzy-logic-based expert system for unmanned landing [J]. Fuzzy Sets and Systems, 1998, 93(2): 145-159.
[129] 杜文莉，钱锋．基于神经网络的实时专家控制系统及其PTA工业应用[J]．控制与决策，2004，20(6)：694-697．
[130] W. Yu and X. -O. Li. Fuzzy identification using fuzzy neural networks with stable learning algorithms [J]. IEEE Trans. on Fuzzy Systems, 2004, 12(3): 411-420.
[131] 涂序彦，潘华，郭江，黄秉宪．生物控制论[M]．北京：科学出版社，1984．
[132] 丁永生，任立红．一种新颖的模糊自调整免疫反馈控制系统[J]．控制与决策，2000，15(4)：443-446，450．
[133] 王焱．模糊免疫PID控制器的设计与仿真[J]．计算机仿真，2002，19(2)：67-69．
[134] D. M. Keenan, J. Licinio and J. D. Veldhuis. A Feedback-controlled Ensemble Model of the Stress-responsive Hypothalamo-pituitary-adrenal Axis [J]. PNAS, 2001, 98(7): 4028-4033.
[135] J. Jung and K. Nam. A dynamic decoupling control scheme for high-speed operation of induction motors [J]. 1EEE Trans. on Industrial Electronics, 1999, 46(1): 100-110.
[136] J. -Y. Hao, W. Chen, H. -M. Li and L. Jia. Feedforward decoupling for multivariable fuzzy control system [J]. Journal of Tianjin University Science and Technology, 2004, 37(5): 396-399.
[137] J. -J. He, S. Y. Yu and J. Zhang. Decoupling control of tension based on pole assignment for temper mill [J]. Journal of Control Theory and Application, 2003, 20(2): 244-248.
[138] R. A. Wright and C. Kravaris. Nonlinear decoupling in the presence of sensor and actuator [A]. Proceedings of the American Control Conference [C], San Diego, California, Jun., 1999, 3: 1503-1507
[139] M. A. Denai and T. Allaoui. Fuzzy decoupling control of UPFC-based power flow compensation [A]. Proceeding of UPEC 2002: 37th International Universities Power Engineering Conference [C], Stafford, United Kingdom, Sep. 9-11, 2002.
[140] Z. Song, P. Sukthankar, Y. -Q. Chen and J. Gu. Progressive fuzzy fusion control of two coupled inverted pendulum [A]. Proceeding of Computational Intelligence in Robotics and Automation, 2003, IEEE International Symposium on [C], Jul. 16-20, 2003, 3: 1457-1462.
[141] H. -B. Liu, S. -Y. Li and T. -Y. Chai. Intelligent decoupling control of power plant main steam pressure and power output [J]. Electrical Power & Energy Systems, 2003, 25: 809-819.
[142] M. -H. Chiang, F. -L. Yang, Y. -N. Chen and Y. P. Yeh. Integrated control of clamping force and energy-saving in hydraulic injection moulding machines using decoupling fuzzy sliding-mode control [J]. Int. J. Adv Manuf Technol, 2005.
[143] S. T. Lin and H. C. Tsai. Impedance control with on-line neural network compensator for dual-arm robots [J]. Journal of Intelligent and Robotic Systems, 1997, 18(1): 87-104.
[144] Z. Ma and A. Jutan. Control of a pressure tank system using a decoupling control algorithm with a neural network adaptive scheme [A]. IEE Proceedings: Control Theory and Applications [C], 2003, 150(4): 389-400.
[145] M. Li, Z. -K. Zhou and L. -L. Shi. The neural network decoupling control based on the intemal model control [A]. Proceeding of WCICA 2004: the 5th World Congress on Intelligent Control and Automation [C], Hangzhou, China, Jun. 15-19, 2004, 3: 2643-2646.
[146] Y. Zhan, Z. -Q. Chen, P. Yang and Z. -Z Yuan. Multivariable nonlinear decoupling control based Proportional-Integral-Derivative on recurrent neural networks [J]. Chinese J. Chem. Eng., 2004, 12(5): 677-681.
[147] K. Warwick, Q. M. Zhu and Z. Ma. A hyperstable neural network for the modeling and control of nonlinear systems [J]. Sadana, 2000, 25(2): 169-180.
[148] A. Hazzab, I. K. Bousserhane and M. Kamli. Design of a fuzzy sliding mode controller by genetic algorithms for induction machine speed control [J]. International Journal of Emerging Electric Power Systems, 2004, 1(2): 1-17.
[149] L. Porter and K. Passino. Genetic adaptive and supervisory control [J]. Int. Journal of Intelligent Control and Systems, 1998, 2(1): 1-41.
[150] Y. -B. Hou. A decoupling control method with improving genetic algorithm [A]. Proceedings of 2002 International Conference on Machine Learning and Cybernetics [C], 2002, 4: 2112-2115.
[151] Y. Quan, and J. Yang. Optimal decoupling control system using kernel method [J]. Journal of Systems Engineering and Electronics, 2004, 15(3): 364-370.
[152] D. Vaes, W. Souverijns, J. D. Cuyper, et al. Decoupling feedback control for improved multivariable vibration test rig tracking [A]. Proceeding of ISMA 2002 [C], 2002, 2: 525-534.
[153] K. Toshiyuki. Tuning method for two-input-two-output I-PD type decoupling control based upon step response shapes [A]. Proceeding of SICE02 [C], 2002, 5: 2759-2762.
[154] W. -P Sung, V. C. Matzen and M. H. Shih. Time domain system identification of unknown initial conditions [J]. Journal of Zhejiang University SCIENCE, 2004, 5(9): 1035-1044.
[155] B. Fanga, A. G. Kelkara, S. M. Joshib and H. R. Pota. Modeling, system identification and control of acoustic-structure dynamics in 3-Denclosures [J]. Control Engineering Practice, 2004, (12): 989-1004.
[156] R. P. Sree and M. Chidambaram. Identification of unstable transfer model with a zero by optimization method [J]. J Indian Inst. Sci., 2002, 82: 219-225.
[157] X. -M Yu. Application of decoupling control in headbox system [J]. Journal of Xi'an University of Science and Technology, 2003, 23(3): 298-301.
[158] H. Deng and H. -X. Li. A novel neural approximate inverse control for unknown nonlinear discrete dynamical systems [J]. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 2005, 35(1): 115-123.
[159] G. L. Plett. Adaptive inverse control of linear and nonlinear systems using dynamic neural networks [J]. IEEE Transactions on Neural Networks, 2003, 14(2): 360-376.
[160] M. Lee and S. Park. Process control using a neural network combined with the conventional PID controllers [J]. Transactions on Control, Automation and Systems Engineering, 2000, 2(3): 196-200.
[161] F. Loquasto and D. E. Seborg. Model predictive controller monitoring based on pattern classification and PCA [J]. American Control Conference, 2003, (3):. 1968-1973.
[162] T. Kato, Y. Ninomiya and I. Masaki. Preceding vehicle recognition based on learning from sample images [J]. IEEE Transactions on Intelligent Transportation Systems, 2002, (3): 252-260.
[163] K. L. Zhou and D. W. Wang. Digital repetitive learning controller for three-phase CVCF PWM inverter[J].IEEE Transportation on Industrial Electronics,2001,48(4) :820-830．
[164] G.Oriolo,S.Panzieri and G.Ulivi.An iterative learning controller for nonholonomic mobile robots[J].International Journal of Robotics Research,1998,17(9) :954-970．
[165] F.H.Dean,F.John,G.Maria and S.James.Fast connectionist learning for trailer backing using a real robot[A].Proceedings of IEEE International Conference on Robotics and Automation[C],1996,1917-1922．
[166] J.S.Alexander,L.C.Baird,W.L.Baker and J.A.Farrell.A design and simulation tool for connectionist learning control systems:Application to autonomous underwater vehicles[A].Proceedings of the Society for Computer Simulation Conference[C],Baltimore,Maryland,Jul.22-24,1991,771-776．
[167] C.Druet,D.Ernst and L.Wehenkel.Application of reinforcement learning to electrical power system closed-loop emergency control[A].Proceedings of Principles of Data Mining and Knowledge Discovery:The 4th European Conference,PKDD 2000[C],Lyon,France,Sep.,2000,1910:86-95．
[168] P.S.Braga.A topological reinforcement learning agent for navigation[J].Neural Computation and Applications,2003,(12) :220-236．
[169] P.K.Leslie,L.L.Michael and W.M.Andrew.Reinforcement learning:A survey[J].Journal of Artificial Intelligence Research,1996,(4) :237-285．
[170] D.B.Gu,H.S.Hu and L.Spacek.Learning fuzzy logic controller for reactive robot behaviours[A].Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics[C],Kobe,Japan,Jul.20-24,2003,1:46-51．
[171] C.Remi.Feedforward neural networks in reinforcement learning applied to high-dimensional motor control[A].Proceedings of Algorithmic Learning Theory:13th International Conference,ALT 2002[C],Lubeck,Germany,Nov.24-26,2002,403-413．
[172] C.Anderson,D.Hittle,A.Katz and R.Kretchmar.Synthesis of reinforcement learning,neural networks,and PI control applied to a simulated heating coil[J].Journal of Artificial Intelligence in Engineering,1997,11(4) :423-431．
[173] S. Norihisa, A. Masaharu and K. Makoto. Control of associative chaotic neural networks using reinforcement learning [A]. Proceedings of Advances in Neural Networks - ISNN 2004: International Symposium on Neural Networks [C], Dalian, China, Aug., 2004, 395-400.
[174] Y. Cang, H. C. Y. Nelson and D. W. Wang. A fuzzy controller with supervised learning assisted reinforcement learning algorithm for obstacle avoidance [J]. IEEE Transactions on Systems, Man, and Cybernetics, 2003, 33 (1): 17-27.
[175] A. O. Esogbue, W. E. Heames and Q. Song. A reinforcement learning fuzzy controller for set-point regulator problems [A]. Proceedings of the IEEE 5th International Fuzzy Systems Conference [C], New Orleans, LA, Sep. 8-11, 1996, 2136-2142.
[176] D. Dasgupta. Artificial immune systems and their applications [M]. Springer-Verlag. Inc. 1999.
[177] N. D. C. Leandro and T. Jonathan. Artificial immune system: A new computational intelligence approach [M]. Springer-Verlag. Inc., 2002.
[178] 蔡美英．医学免疫学[M]．北京：科学出版社，2002．
[179] Z. -Q. Qi and S. -M. Song. A novel immune feedback control algorithm and its applications [A]. Proceedings of Genetic and Evolutionary Computation Conference-GECCO 2004 [C], Seattle, WA, USA, Jun. 26-30, 2004, 318-320.
[180] K. Motohiro, S. Minoru and T. Kazuhiko. Adaptive learning method of network controller using an immune feedback [A]. Proceedings of the 1999 IEEE/ASME International Conference Advanced Intelligent Mechatronics [C], Atlanta, USA, Sep. 19-23, 1999, 641-646.
[181] K. Takashi and T. Yamada. Application of an immune feedback mechanism to control system [J]. JSME International Journal, Series C, 1998, 41(2): 184-191.
[182] D. H. Kim. PID controller tuning of a boiler control system using immune algorithm typed neural network [A]. Proceedings of Computational Science-ICCS 2004: 4th International Conference [C], Krakow, Poland, Jun. 6-9, 2004, 695-698.
[183] D. H. Kim and J. I. Park. Intelligent PID control by immune algorithms based fuzzy rule auto-tuning [J]. Lecture Notes in Computer Science, 2003, 2715: 474-482.
[184] A. Naim and A. H. Sadeq. Genetic design of fuzzy mapped PID controller for non-linear plants [J]. Information and Technology Journal, 2004, 3(1): 44-48.
[185] B. Y. Kim, G. J. Nam, H. S. Ryu and J. W. Lee. Optimization of filling process in RTM using genetic algorithm [J]. Korea-Australia Rheology Journal, 2000, 12(1): 83-92.
[186] M. I. Jordan and C. M. Bishop. Neural networks [J]. ACM Computing Surveys, 1996, 28(1): 73-75.
[187] C. M. Ennett, M. Frize and C. R. Walker. Influence of missing values on artificial neural network performance [A]. Proceedings of Medinfo 2001 [C], London, UK, Sep. 2-5, 2001, 10(1): 449-53.
[188] X. Yao and Y. Liu. Making use of population information in evolutionary artificial neural networks [J]. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 1998, 28(3): 417-425.
[189] X. Yao and Y. Liu. Towards designing artificial neural networks by evolution [J]. Applied Mathematics and Computation, 1998, 91(1): 83-90.
[190] J. C. Tay and A. Jhavar. CAFISS: a complex adaptive framework for immune system simulation [A]. Proceeding of 2005 ACM Symposium on Applied Computing (SAC) [C], Santa Fe, New Mexico, USA, Mar. 13-17, 2005, 158-164.
[191] S. Usan, A. C. John, C. G. Johnson, et al. Artificial immune systems and the grand challenge for non-classical computation [A]. Proceedings of the 2003 International Conference on Artificial Immune Systems [C], Lecture Notes in Computer Science, 2003, 2787: 204-216.
[192] S. Stepney, R. Smith, J. Timmis and A. Tyrrell. Towards a conceptual framework for artificial Immune Systems [A]. Proceedings of Third International Conference on Artificial Immune Systems [C], Lecture Notes in Computer Science, 2004, 3239: 3-64.
[193] J. C. Wommack, A. Salinas, R. H. Melloni Jr and Y. Delville. Behavioural and neuroendocrine adaptations to repeated stress during puberty in male golden hamsters [J]. Journal of Neuroendocrinology, 2004, 16 (9): 767-775.
[194] P. Haake, M. Schedlowski, M. S. Exton, et al. Acute neuroendocrine response to sexual stimulation in sexual offenders [J]. The Canadian Journal of Psychiatry, 2003, 48(4): 265-271.
[195] J. K. Payne. A neuroendocrine-based regulatory fatigue model [J]. Biological Research for Nursing, 2004, 6(2): 141-150.
[196] S. Cayzer and U. Aickelin. A recommender system based on the immune network[A]. Proceedings of 2002 Congress on Evolutionary Computation (CEC) [C], Honolulu, USA, May 12-17, 2002, 807-813.
[197] K. Motohiro, S. Minoru and T. Kazuhiko. Adaptive learning method of network controller using an immune feedback [A]. Proceedings of the 1999 IEEE/ASME International Conference Advanced Intelligent Mechatronics[C], Atlanta, USA, Sep. 19-23, 1999, 641-646.
[198] B. Liu, L. -H. Ren and Y. S. Ding. A novel intelligent controller based on modulation of neuroendocrine system [A]. Proceedings of the 2nd International Symposium on Neural Networks (ISNN 2005) [C], Lecture Notes in Computer Science, 2005, 3498(3): 119-124.
[199] B. Salemi, W. Shen and P. Will. Hormone controlled metamorphic robots [A]. Proceedings of the IEEE International conference on Robotics & Automation 2001[C], Seoul, Korea, 2001, 4194-4199.
[200] W. -M. Shen, B. Salemi and P. Will. Hormones for self-reconfigurable robots [A]. Proceedings of the International conference on Intelligent Autonomous Systems (IAS-6) [C], Venice, Italy, 2000, 918-925.
[201] 陈国初．微粒群优化算法及其在软测量建模中的应用[D]．华东理工大学博士学位论文，2006，5．
[202] L. G. Bushnell. Networks and control [J]. IEEE Control System Magazine, 2001, 21(1): 22-23.
[203] F. -L. Lian. Network design consideration for distributed control system [J]. IEEE Transactions on Control System Technology, 2002, 10(2): 297-307.
[204] G. A. Mintchell, C. Huitema. Ethernet's in control [J]. Control Engineering, 2000, 47(5): 46-54.
[205] S. Vitturi. On the use of Ethernet at low level of factory communication systems[J]. Computer Standards and Interfaces, 2001, (4): 267-277.
[206] F. P. Maturanaa, P. Tichy, P. Slechta, et al. Distributed multi-Agent architecture for automation systems [J]. Expert Systems with Applications, 2004, 26: 49-56.
[207] E. Camponogara and S. Talukdar. Designing communication networks for distributed control agents [J]. European Journal of Operational Research, 2004, 153: 544-563.
[208] R. Vadigepalli. Local intelligent control in biological systems and industrial process [EB/OL]. http://cdtower.hdpu.edu.cn/newwww/default.htm, Mar. 14, 2004
[209] 李力雄．基于网络整定的控制系统中网络诱导延时的分析及解决方法研究[D]．上海大学博士学位论文，2005，5．
[210] N. B. Almutairi, M. Y. Chow and Y. Tipsuwan. Network-based controlled DC motor with Fuzzy compensation [A]. Proceeding of the 27th Annual Conference of the IEEE Industrial Electronics Society [C], Nov. 29- Dec. 2, 2001, 3: 1844-1849.
[211] 方崇智，萧德云．过程辨识[M]．北京：清华大学出版社，1988．
[212] 王耀南．智能控制系统 模糊逻辑 专家系统 神经网络控制[M]．长沙：湖南大学出版社，1996．
[213] 金振林，高峰，李金良．并联3—2—1结构新型操作手及其承载能力分析[J]．中国机械工程，2002，13(2)：105-108．
[214] Attila L. Bencsik. Appropriate mathematical model of DC servo motors applied in SCARA robots [J]. Acta Polytechnica Hungarica, 2004, 1(2): 99-111.
[215] J. Santana, J. L. Naredo, F. Sandoval, I. Grout and O. J. Argueta. Simulation and constr-uction of a speed control for a DC series motor [J]. Mechatronics, 2002, 12(9-10): 1145-1156.
[216] A. Sevinc. A full adaptive observer for DC servo motors [J]. Turk J Elec Engin, 2003, 11(2): 117-130.
[217] M. J. Burridge and Z. -H. Qu. An improved nonlinear control design for series DC motors [J]. Computers & Electrical Engineering, 2003, 29(2): 273-288.
[218] J. Jugo. Real-time control of a DC motor using scilab and RTAI [EB/OL], http://scilabsoft.inria.fr/events/scilab2004/final_paper/9-JosuJugo_scilabRTA1.pdf, 2006, May 4.
[219] W. L. Xu and J. D. Han. Joint acceleration feedback control for robots: analysis, sensing and experiments [J]. Robotics and Computer-Integrated Manufacturing, 2000, 16(5): 307-320.
[2] 陈宗海．智能自动化技术的现状与发展趋势[J]．自动化博览，2001，18(2)：4-7．
[3] G. Stephanopoulos and C. Han. Intelligent systems in process engineering: a review[J]. Computers & Chemical Engineering, 1996, 20(6-7): 743-791.
[4] M. Clive and W. Winston. Development of an intelligent controller for power generators [J]. Journal of Physics: Conference Series, 2005, 15: 306-310.
[5] M. Lind. Status and challenges of intelligent plant control [J]. Annual Reviews in Control, 1996, 20: 23-41.
[6] 戴汝为，王玉．关于智能系统的综合集成[J]．科学通报，1993，38(14)：1249-1256．
[7] 盛万兴，戴汝为．关于智能控制[J]．电子学报，1999，27(5)：86-89．
[8] M. A. Thomas. Intelligent "control" applications in the process industries [J]. Annual Reviews in Control, 2002, 26(1): 75-86.
[9] 蔡自兴．智能控制四元结构的研究[J]．高技术通讯，1996．6(7)：19-23．
[10] L. A. Zadeh. Fuzzy sets [J]. Information and Control, 1965, (8): 330-353.
[11] K. S. Narendra. Intelligent control [J]. Control Systems Magazine, 1991, 11(1): 39-40.
[12] 刘向杰，周孝信，柴天佑．模糊控制研究的现状与新发展[J]．信息与控制，1999，28(4)：283-292．
[13] W. J. Wang and B. Y. Tang. A fuzzy adaptive method for intelligent control [J]. Expert Systems with Applications, 1999, 16(1): 43-48.
[14] E. H. Mamdani and B. R. Gains. Fuzzy reasoning and its applications [M]. London: Academic, 1981.
[15] R. D. Sarma and N. Ajamai. Fuzzy nets and their application [J]. Fuzzy Sets and Systems, 1992, 45: 41-51.
[16] K. S. Fu. Learning control system and intelligent control system: an intersection of artificial intelligence and automatic control [J]. IEEE Trans. on AC, 1971, 16(1): 70-72.
[17] S. I. Gallant. Network learning and expert systems [J]. Communications of the ACM, 1988, 31(2): 152-169.
[18] 张再兴，孙增沂．关于专家控制[J]．信息与控制，1995，24(3)：167-172．
[19] 邱东强．神经网络控制的现状与展望[J]．自动化与仪器仪表，2001，(5)：1-7．
[20] D. de Ridder, R. P. W. Duin, P. W. Verbeek and L. J. van Vliet. The applicability of neural networks to non-linear image processing [J]. Pattern Analysis & Applications. 1999, (2): 111-128.
[21] 商琳，王金根，姚望舒，陈世福．一种基于多进化神经网络的分类方法[J]．软件学报，2005，16(9)：1577-1583．
[22] S. -Y. Yun, S. Namkoong, J. -H. Rho, etc. A performance evaluation of neural network models in traffic volume forecasting [J]. Mathl. Comput. Modeling, 1998, 27(9-11): 293-310.
[23] C. T. Lin and C. S. G. Lee. Neural: neural-network-based fuzzy logic control and decision system [J]. IEEE Trans. on Computer, 1991, 40(12): 1320-1336.
[24] F. Mauricio and G. Fernando. Design of fuzzy system using neurofuzzy networks[J]. IEEE Trans. on Neural Networks. 1999, 10(4): 815-827.
[25] C. T. Chen and S. T. Peng. Intelligent process control using neural fuzzy techniques [J]. Journal of Process Control, 1999, 9(6): 493-503.
[26] 刘曙光，费佩燕，侯志敏．遗传算法的进展与展望[J]．现代电子技术，2000，(6)：83-87．
[27] P. J. Fleming and R. C. Purshouse. Evolutionary algorithms in control systems engineering: a survey [J]. Control Engineering Practice, 2002, 10(11): 1223-1241.
[28] H. S. Yasmin. On Genetic Algorithms and their Applications [J]. Handbook of Statistics, 2005, 24,: 359-390.
[29] 段玉倩，贺家李．遗传算法及其改进[J]．电力系统及其自动化学报，1998，10(1)：39-52．
[30] E. M Norberto, F. Takeshi, Tomonori, et al. A study on the discovery of relevant fuzzy rules using pseudo-bacterial genetic algorithms [J]. IEEE Trans. on Industry Applications. 1999, 46(6): 1080-1089.
[31] X. Ning and L. Lathar. A new genetic based approach to fuzzy controller design and its application [A]. Proceedings of the 1998 IEEE International Conference on Control Applications [C]. Trieste, Italy, Sept. 1-4, 1998, 937-941.
[32] R. M. Dimeo and K. Y. Lee. Genetics-based control of a MIMO boiler turbine plant [A]. Proceedings of the 33rd Conference on Decision and Control [C]. Lake Buena Vista, Florida, Dec. 14-16, 1994, 3512-3517.
[33] S. S. Mei, Z. Huang and K. L. Fang. A neural network controller based on genetic algorithms [A]. Proceedings of IEEE International Conference on Intelligent Processing Systems [C], Beijing, China, Oct. 28-31, 1997, 1624-1628.
[34] Z. J. Liu and M. Sugisaka. A genetic algorithm approach used to generate the neural network structures [A]. Proceedings of the 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems [C], 1999, 763-768.
[35] B. Khaled and T. Faouzi. Genetic algorithm for the design of a class fuzzy controller: an alternative approach [J]. IEEE Trans. on Fuzzy Systems. 2000, 8(4): 398-405.
[36] M. Srinivas. Adaptive probabilities of crossover and mutation in genetic algorithms [J]. IEEE Trans. on Systems, Man and Cybernetics, 1994, 24(4): 656-667.
[37] 丁永生，任立红．人工免疫系统：理论与应用[J]．模式识别与人工智能，2000，13(1)：52-59．
[38] L. -C. Jiao and L. Wang. Novel genetic algorithm based on immunity [J]. IEEE Trans on Systems, Man and Cybernetics-Part A: Systems and Humans, 2000, 30(5): 552-561.
[39] 肖人彬，王磊．人工免疫系统：原理、模型、分析及展望[J]．计算机学报，2002，25(12)：1281-1293．
[40] L. N. De Castro and F. J. Vov Zuben. An immuneological approach to initialize centers of radial basis function neural networks [A]. Proceedings of V Brazilian Confon Neural Networks[C]. Rio de Janeiro, Brazil, 2001, 79-84.
[41] J. G. Zheng, L. Wang and L. C. Jiao. An immune algorithm for generalized rule induction [A]. Proceedings of CIE Int Con of Radar Proc [C]. Beijing, China, 2001, 1023-1026.
[42] T. Gong and Z. X. Cai. Mobile immune-robot model [A]. Proceeding of IEEE Int Conf on Robotics, Intelligent Systems and Signal Proc[C]. Changsha, China, Oct. 8-13, 2003, 1091-1096.
[43] J. Timmis and M. Neal. Artificial homeostasis: integrating biologically inspired computing [EB/OL], www.cs.kent.ac.uk/pubs/2003/1586/content.pdf, Apr. 15, 2004.
[44] P. Vargas, R. Moioli, LN De Castro, et al. Artificial homeostasis: a novel approach [A], Proceedings of ECAL 2005 [C], LNAI, 2005, 3630, 754-763.
[45] 黄国锐．人工内分泌模型及其应用研究[D]．中国科学与技术大学博士学位论文，2003，6．
[46] H. Besedovsky and E. Sorkin. Network of immune-neuroendocrine interactions [J]. Clinical and Experimental Immunology, 1977, 27: 1-12.
[47] G. Dan and S. B. Lall. Neuroendocrine modulation of immune system [J]. Indian Journal of Pharmacology, 1998, 30: 129-140.
[48] B. Brazzini, I. Ghersetich, J. Hercogova and T. Lotti. The neuro-immuno-cutaneousendocrine network: relationship between mind and skin [J]. Dermatologic Therapy, 2003, 16: 123-131.
[49] W. Savino and M. Dardenne. Neuroendocrine control of thymus physiology [J]. Endocrine Reviews, 2000, 21(4): 412-443.
[50] D. M. Keenan and J. D. Veldhuis. A biomathematical model of time-delayed feedback in the human male hypothalamic-pituitary-leydig cell axis [J]. American Journal of Physiology, 1998, 275: 157-176.
[51] L. S. Farhy. Modeling of oscillations of endocrine networks with feedback [J]. Methods Enzymol, 2004, 384: 54-81.
[52] P. Vargas, M. R. L. oiolil, N. D. Castro, et al. Artificial homeostatic system: a novel approach [A]. Proceeding of ECAL 2005 [C], LNAI, 2005, 3630: 754-764.
[53] 马勇，许晓鸣，张卫东．一种改进的基于再励学习算法的模糊神经BOXES控制系统[J]．模糊系统与数学，2000，14(1)：78-83．
[54] P. E. Macrossan, H. A. Abbass, K. Mengersen, et al. Bayesian neural network learning for prediction in the Australian dairy industry [J]. Lecture Notes in Computer Science, 1999, 1642: 395-406.
[55] K. S. Fu and M. Waltz. A heuristic approach to reinforcement learning control system [J]. IEEE Trans. on AC, 1965, 10(4): 390-398.
[56] H. O. Besedovsky and E. Sorkin. Network of immune-neuroendocrine interaction[J]. Clinical and Experimental Immunology, 1977, 27: 1-12.
[57] 谢蜀生．神经-内分泌-免疫网络[J]．科技导报，1994，(8)：11-12，64．
[58] 胡格，穆祥，段慧勤，杨佐君，高立云．免疫、神经和内分泌系统间的关系[J]，动物医学进展，2003，24(1)：5-7．
[59] 王玢，左明雪．人体及动物生理学[M]．北京：高等教育出版社，2001，7．
[60] I. M. Kvetnoy. Neuroimmunoendocrinology: Where is the field for study? [J]. Neuroendocrinology Letters, 2002, 23: 119-120.
[61] I. G. Akmaev. Problems and prospects in development of neuroimmunoendocrinology [J]. Problemy Endokrinologii -Moskva- Meditsina, 1999, 45(5): 3-7.
[62] 关新民．医学神经生物学纲要[M]．北京：科学出版社，2003：267-285．
[63] R. P. Lippmann. An introduction to computing with neural nets [J], IEEE ASSP Magazine, 1987, 4(2): 4-22.
[64] 朱大奇．人工神经网络研究现状及其展望[J]，江南大学学报(自然科学版)，2004，3(1)：103-110．
[65] 吴宏岐，张军利，周妮娜．基于神经网络的智能控制技术及应用[J]．信息技术，2004，28(1)：1-3．
[66] M. Dornier, B. Heyd and M. Danzart. Evaluation of the simplex method for training simple multilayer neural networks [J]. Neural Comput & Applic, 1998, (7): 107-114.
[67] M. Dornier, B. Heyd and M. Danzart. Evaluation of the simplex method for training simple multilayer neural networks [J]. Neural Comput & Applic, 1998, (7): 107-114.
[68] Y. -X. Huang, Y. Wang, W. G. Zhou, et al. A fuzzy neural network system based on generalized class cover and particle swarm optimization [J]. Lecture Notes in Computer Science, 2005, 3645: 119-128.
[69] M. Inoue and A. Nagayoshi. A chaos neural-computer [J]. Physical Letter A, 1991, 158(8): 373-376.
[70] M. Inoue and K. Nakamoto. Epilepsy in a chaos neural-computer model, chaos in biology and medicine [J]. SPLE, 1993, 236: 77-84.
[71] D. Ventura and R. Martinez. Quantum associated memory [J]. Information Sciences, 2000, 124: 147-148.
[72] D. Ventura. Quantum computing and neural information processing [J]. Information Sciences, 2000, 128: 273-296.
[73] A. D. Mcaulay and M. C. Wang. Optical hetero associative memory using spatial light rebroadcasters [J]. Applied Optical, 1990, 29(14): 2067-2073.
[74] I. Kucuk and N. Derebasi. Prediction of power losses in transformer cores using feed forward neural network and genetic algorithm [J]. Measurement, 2006, 39(7): 605-611.
[75] A. Blanco, M. Delgado and M. C. Pegalajar. A genetic algorithm to obtain the optimal recurrent neural network [J]. International Journal of Approximate Reasoning, 2000, 23(1): 67-83.
[76] D. D. Ridder, R. P. W. Duin, P. W. Verbeek and L. J. van Vliet. The applicability of neural networks to non-linear image processing [J]. Pattern Analysis & Applications, 1999, (2): 111-128.
[77] Jasmina Arifovic and Ramazan Gencay. Using genetic algorithms to select architecture of a feedforward artificial neural network [J]. Physica A: Statistical Mechanics and its Applications, 2001, 289(3-4): 574-594.
[78] G. -C. Chen and J. S. Yu. Particle swarm optimization neural network and its application in soft-sensing modeling [J]. Lecture Notes in Computer Science, 2005, 3611, 610-617.
[79] B. Chaudhuri and J. M. Modak. Optimization of fed-batch bioreactor using neural network model [J]. Bioprocess Engineering, 1998, 19: 71-79.
[80] J. L. H. John. Categorization of fetal heart rate patterns using neural networks [J]. Journal of Medical Systems, 2001, 25(4): 269-276.
[81] M. Perus and P. Ecimovic. Memory and pattern recognition in associative neural networks [J]. International Journal of Applied Science and Computation, 1998, (4): 283-310.
[82] S. Ferrari and R, F. Stengel. Smooth function approximation using neural networks [J]. IEEE Transactions on Neural Networks, 2005, 16 (1): 24-38.
[83] 文绍纯．基于遗传算法的人工神经网络的应用综述[J]．自动化与仪器仪表，2001，(6)：1-4．
[84] 陆德源，马宝骊．现代免疫学[M]．上海：上海科技教育出版社，1998，12．
[85] N. D. C. Leandro and J. V. Z. Femando. Learning and optimization using the clonal selection principle [J]. IEEE Trans on Evolutionary Computation, 2002, 6(3): 306-313.
[86] A. S. Perelson. Immune network theory [J]. Immunological Review, 1986, (10): 5-36.
[87] A. Ishiguro, S. Ichhikawa and Y. Uchikawa. A gait acquisition of 6-legged walking robot using immune networks [A], Proceeding of IROS'94 [C], 1994, 2: 1034-1041.
[88] A. Ishiguro, Y. Watanabe, S. Ichikawa, et al. Gait control of hexapod walking robots using mutual-coupled immune network [J]. Advanced Robotics, 1996, 10(2): 179-195.
[89] N. K. Jeme. Towards a network theory of the immune System [J]. Ann Immunol, 1974, 125C: 373-389.
[90] J. D. Farmer, N. H. Packard and A. S. Prelson. The immune system, adaptation, and machine learning [J]. Physical D, 1986, 22: 187-204.
[91] S. A. Hofmeyr and S. Forrest. Immunity by design: an artificial immune system [A]. Proceeding of GECCO'99 [C], 1999, 1289-1296.
[92] R. N. Germain. MHC-associated antigen processing, presentation, and recognition, adolescence, maturity and beyond [J]. The Immunologist, 1995, (6): 185-190.
[93] J. Timmis and M. Neal. A resource limited artificial system for data analysis [J]. Knowledge-Based-Systems, 2001, 14: 121-130.
[94] Z. Tang, T. Yamaguchi, K. Tashima, et al. Multiple-valued immune network model and its simulation [A]. Proceeding of 27th Int Symposium on Multiple-valued Logic [C], Autigonish, Canada, 1997, 233-238.
[95] D. Castro and J. Fernando. An evolutionary immune network for data clustering [A]. Proceeding of the IEEE. SBRN [C], 2000, 84-89.
[96] F. T. Vertosick and R. H. Kelly. Immune network theory: a role for paralled distributed processing [J]. Immunlogy, 1989, 66: 1-7.
[97] J. S. Chun, M. K. Kim, H. K. Jung, et al. Shape optimization of electromagnetic device using immune algorithm [J]. IEEE Trans. on Magnetics, 1997, 33(2): 1876-1879.
[98] Y. Ishida and N. Adachi. Active noise control by an immune algorithm: adaptation immune system as an evolution [A]. Proceeding of ICEC 96 [C], 1996, 150-153.
[99] P. Dhaeseleer, S. Forrest and P. Helman. An immunological approach to change detection algorithms: Analysis and implications [A]. Proceeding of IEEE Symposium on Security and Privacy [C], Las Alamitos, CA, USA, 1996, 110-119.
[100] L. N. De Castro and F. J. VonZuben. Clonal selection algorithm with engineering applications [A]. Proceeding of GECCO'00 [C], LasVegas, Nevada, USA, 2000, 36-37.
[101] Y. -S. Ding and L. -H. Ren. Fuzzy self-tuning immune feedback controller for tissue hyperthermia [A]. Proceeding of Fuzzy 1EEE 2000: The Ninth IEEE International Conference [C], May 7-10, 2000, 1: 534-538.
[102] B. Liu and Y. -S. Ding. An intelligent controller based on primary-secondary responding mechanism of immune system [A]. Proceeding of the 7th International Conference on Computational Intelligence and Natural Computing (CIN-3) [C], 2005, 467-470.
[103] D. H. Kim. PID controller tuning of a boiler control system using immune algorithm typed neural network [A]. Proceeding of Computational Science - ICCS 2004: the 4th International Conference [C], Krakow, Poland, Jun. 6-9, 2004, 695-698.
[104] D. H. Kim and J. I. Park. Intelligent PID control by immune algorithms based fuzzy rule auto-tuning [J]. Lecture Notes in Computer Science, 2003, 2715: 474-482.
[105] G. -C. Luh and C. -H. Chueh. Multi-modal topological optimization of structure using immune algorithm [J]. Computer Methods in Applied Mechanics and Engineering, 2004, 193(36-38): 4035-4055.
[106] 谢启文．现代神经内分泌学[M]．上海：上海医科大学出版社，1999．
[107] D. M. Keenan, J. Licinio and J. D. Veldhuis. A feedback-controlled ensemble model of the stress-responsive hypothalamo-pituitary-adrenal axis [J]. PNAS, 2001, 98(7): 4028-4033.
[108] L. S. Farhy, M. Straume, et al. A construct of interactive control of the GH axis in the male [J]. Am J Physiol Regulatory Integration Comp Physiol, 2001, 281: 38-51.
[109] L. S. Farhy, Modeling of oscillations of endocrine networks with feedback [EB/OL]. http://www.people.virginia.edu/～lsf8n/Educational.pdf, May 14, 2004.
[110] E. A. Kraitz. Hormonal control for behavior: amines and the biasing of behavioral output in lobsters [J]. Science, 1988, 241: 1775-1781.
[111] R. A. Brooks. Integrated systems based on behaviours [J]. SIGART Bulletion, 1991, 2(4): 46-50.
[112] D. M. Keenan, J. Licinio and J. D. Veldhuis. A feedback-controlled ensemble model of the stress-responsive hypothalamo-pituitary-adrenal axis [J]. PNAS, 2001, 98(7): 4028-4033.
[113] B. Liu, K. R. Hao and Y. S. Ding. A nonlinear optimized controller based on modulation of testosterone [A]. Proceeding of The Third International Conference on Computational Intelligence, Robotics and Autonomous Systems (CIRAS 2005) [C], Singapore, Dec. 14-16, 2005.
[114] B. Liu and Y. S. Ding. A decoupling control based on the bi-regulation principle of growth hormone [A]. Proceeding of 2005 ICSC Congress on Computational Intelligence: Methods & Application 2005 (CIMA'05) [C], Istanbul, Turkey, Dec. 15-17, 2005.
[115] B. Liu, Y. S. Ding and J. H. Wang, An intelligent controller inspired from neuroendocrine-immune system [A]. Proceeding of 2006 International Conference on Intelligent Systems & Knowledge Engineering (ISKE2006) [C], Shanghai, China, Apr. 6-7, 2006.
[116] 苏小红，杨博，王亚东．基于进化稳定策略的遗传算法[J]．软件学报，2003，14(11)：1863-1868．
[117] 刘福才，潘江华，路平立，裴润．一种改进的变焦遗传算法[J]．信息与控制，2004，33(1)：82-84．
[118] D. W. Wang and S. C. Fang. A genetic-based approach for aggregated production planning in a fuzzy environment [J]. IEEE Trans. on Systems, Man, and Cybernetics-Part A: Systems and Humans, 1997, 27(5): 636-645.
[119] C. Co Chen and C. -C. Wang. Self-generating rule-mapping fuzzy controller design using a genetic algorithm [A]. Proceeding of IEE Proc. -Control Theory Apple [C], 2002, 149(2): 143-148.
[120] H. K. Lam, S. H. Ling, F. H. F. Leung, et al. Optimal and stable fuzzy controllers for nonlinear systems subject to parameter uncertainties using genetic algorithms [J]. IEEE Transactions on Industrial Electronics, 2004, 51(1): 172-182.
[121] Y. S. Zhou and L. Y. Lai. Optimal design for fuzzy controllers by genetic algorithms [J]. EEE Transaction on Industry Applications, 2000, 36(1): 93-97.
[122] 李建华，王孙安．最优家族遗传算法[J]．西安交通大学学报，2004，38(1)：77-80．
[123] 谢晓锋，张文俊，杨之廉．一种防止浮点遗传算法早熟收敛的父代选择策略[J]．控制与决策，2002，17(5)：625-634．
[124] J. J. Grefenstete. Optimization of control parameters for genetic algorithms [J]. IEEE Trans. on systems, Man and Cybernetics, 1986, 16(1): 122-128.
[125] Y. -M. Li, X. -P. Liu and Z. -Y. Peng. A fuzzy method approach for identification of joint parameters of mechanical structures [J]. Journal of Mechanical Engineering, 2002, 53(5): 280-288.
[126] M. R. M. Rizk, El-Arabaey and H. S. Khaddam. An algorithm for optimum stability region of fuzzy control systems using genetic algorithms [A]. Proceedings of the American Control Conference [C], Arlington, Jun. 25-27, 2001, 192-197.
[127] S. Nishida, R. Palmer, C. Slaughterbeck and S. Walker. An expert system for control curve evaluation during drought [A]. Proceeding of the 17th Annual National Conference, Water Resources Planning and Management Division [C], ASCE, Ft Worth, Texas, 1990, 294-297.
[128] M. Livchitz, A. Abershitz, U. Soudak and A. Kande. Development of an automated fuzzy-logic-based expert system for unmanned landing [J]. Fuzzy Sets and Systems, 1998, 93(2): 145-159.
[129] 杜文莉，钱锋．基于神经网络的实时专家控制系统及其PTA工业应用[J]．控制与决策，2004，20(6)：694-697．
[130] W. Yu and X. -O. Li. Fuzzy identification using fuzzy neural networks with stable learning algorithms [J]. IEEE Trans. on Fuzzy Systems, 2004, 12(3): 411-420.
[131] 涂序彦，潘华，郭江，黄秉宪．生物控制论[M]．北京：科学出版社，1984．
[132] 丁永生，任立红．一种新颖的模糊自调整免疫反馈控制系统[J]．控制与决策，2000，15(4)：443-446，450．
[133] 王焱．模糊免疫PID控制器的设计与仿真[J]．计算机仿真，2002，19(2)：67-69．
[134] D. M. Keenan, J. Licinio and J. D. Veldhuis. A Feedback-controlled Ensemble Model of the Stress-responsive Hypothalamo-pituitary-adrenal Axis [J]. PNAS, 2001, 98(7): 4028-4033.
[135] J. Jung and K. Nam. A dynamic decoupling control scheme for high-speed operation of induction motors [J]. 1EEE Trans. on Industrial Electronics, 1999, 46(1): 100-110.
[136] J. -Y. Hao, W. Chen, H. -M. Li and L. Jia. Feedforward decoupling for multivariable fuzzy control system [J]. Journal of Tianjin University Science and Technology, 2004, 37(5): 396-399.
[137] J. -J. He, S. Y. Yu and J. Zhang. Decoupling control of tension based on pole assignment for temper mill [J]. Journal of Control Theory and Application, 2003, 20(2): 244-248.
[138] R. A. Wright and C. Kravaris. Nonlinear decoupling in the presence of sensor and actuator [A]. Proceedings of the American Control Conference [C], San Diego, California, Jun., 1999, 3: 1503-1507
[139] M. A. Denai and T. Allaoui. Fuzzy decoupling control of UPFC-based power flow compensation [A]. Proceeding of UPEC 2002: 37th International Universities Power Engineering Conference [C], Stafford, United Kingdom, Sep. 9-11, 2002.
[140] Z. Song, P. Sukthankar, Y. -Q. Chen and J. Gu. Progressive fuzzy fusion control of two coupled inverted pendulum [A]. Proceeding of Computational Intelligence in Robotics and Automation, 2003, IEEE International Symposium on [C], Jul. 16-20, 2003, 3: 1457-1462.
[141] H. -B. Liu, S. -Y. Li and T. -Y. Chai. Intelligent decoupling control of power plant main steam pressure and power output [J]. Electrical Power & Energy Systems, 2003, 25: 809-819.
[142] M. -H. Chiang, F. -L. Yang, Y. -N. Chen and Y. P. Yeh. Integrated control of clamping force and energy-saving in hydraulic injection moulding machines using decoupling fuzzy sliding-mode control [J]. Int. J. Adv Manuf Technol, 2005.
[143] S. T. Lin and H. C. Tsai. Impedance control with on-line neural network compensator for dual-arm robots [J]. Journal of Intelligent and Robotic Systems, 1997, 18(1): 87-104.
[144] Z. Ma and A. Jutan. Control of a pressure tank system using a decoupling control algorithm with a neural network adaptive scheme [A]. IEE Proceedings: Control Theory and Applications [C], 2003, 150(4): 389-400.
[145] M. Li, Z. -K. Zhou and L. -L. Shi. The neural network decoupling control based on the intemal model control [A]. Proceeding of WCICA 2004: the 5th World Congress on Intelligent Control and Automation [C], Hangzhou, China, Jun. 15-19, 2004, 3: 2643-2646.
[146] Y. Zhan, Z. -Q. Chen, P. Yang and Z. -Z Yuan. Multivariable nonlinear decoupling control based Proportional-Integral-Derivative on recurrent neural networks [J]. Chinese J. Chem. Eng., 2004, 12(5): 677-681.
[147] K. Warwick, Q. M. Zhu and Z. Ma. A hyperstable neural network for the modeling and control of nonlinear systems [J]. Sadana, 2000, 25(2): 169-180.
[148] A. Hazzab, I. K. Bousserhane and M. Kamli. Design of a fuzzy sliding mode controller by genetic algorithms for induction machine speed control [J]. International Journal of Emerging Electric Power Systems, 2004, 1(2): 1-17.
[149] L. Porter and K. Passino. Genetic adaptive and supervisory control [J]. Int. Journal of Intelligent Control and Systems, 1998, 2(1): 1-41.
[150] Y. -B. Hou. A decoupling control method with improving genetic algorithm [A]. Proceedings of 2002 International Conference on Machine Learning and Cybernetics [C], 2002, 4: 2112-2115.
[151] Y. Quan, and J. Yang. Optimal decoupling control system using kernel method [J]. Journal of Systems Engineering and Electronics, 2004, 15(3): 364-370.
[152] D. Vaes, W. Souverijns, J. D. Cuyper, et al. Decoupling feedback control for improved multivariable vibration test rig tracking [A]. Proceeding of ISMA 2002 [C], 2002, 2: 525-534.
[153] K. Toshiyuki. Tuning method for two-input-two-output I-PD type decoupling control based upon step response shapes [A]. Proceeding of SICE02 [C], 2002, 5: 2759-2762.
[154] W. -P Sung, V. C. Matzen and M. H. Shih. Time domain system identification of unknown initial conditions [J]. Journal of Zhejiang University SCIENCE, 2004, 5(9): 1035-1044.
[155] B. Fanga, A. G. Kelkara, S. M. Joshib and H. R. Pota. Modeling, system identification and control of acoustic-structure dynamics in 3-Denclosures [J]. Control Engineering Practice, 2004, (12): 989-1004.
[156] R. P. Sree and M. Chidambaram. Identification of unstable transfer model with a zero by optimization method [J]. J Indian Inst. Sci., 2002, 82: 219-225.
[157] X. -M Yu. Application of decoupling control in headbox system [J]. Journal of Xi'an University of Science and Technology, 2003, 23(3): 298-301.
[158] H. Deng and H. -X. Li. A novel neural approximate inverse control for unknown nonlinear discrete dynamical systems [J]. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 2005, 35(1): 115-123.
[159] G. L. Plett. Adaptive inverse control of linear and nonlinear systems using dynamic neural networks [J]. IEEE Transactions on Neural Networks, 2003, 14(2): 360-376.
[160] M. Lee and S. Park. Process control using a neural network combined with the conventional PID controllers [J]. Transactions on Control, Automation and Systems Engineering, 2000, 2(3): 196-200.
[161] F. Loquasto and D. E. Seborg. Model predictive controller monitoring based on pattern classification and PCA [J]. American Control Conference, 2003, (3):. 1968-1973.
[162] T. Kato, Y. Ninomiya and I. Masaki. Preceding vehicle recognition based on learning from sample images [J]. IEEE Transactions on Intelligent Transportation Systems, 2002, (3): 252-260.
[163] K. L. Zhou and D. W. Wang. Digital repetitive learning controller for three-phase CVCF PWM inverter[J].IEEE Transportation on Industrial Electronics,2001,48(4) :820-830．
[164] G.Oriolo,S.Panzieri and G.Ulivi.An iterative learning controller for nonholonomic mobile robots[J].International Journal of Robotics Research,1998,17(9) :954-970．
[165] F.H.Dean,F.John,G.Maria and S.James.Fast connectionist learning for trailer backing using a real robot[A].Proceedings of IEEE International Conference on Robotics and Automation[C],1996,1917-1922．
[166] J.S.Alexander,L.C.Baird,W.L.Baker and J.A.Farrell.A design and simulation tool for connectionist learning control systems:Application to autonomous underwater vehicles[A].Proceedings of the Society for Computer Simulation Conference[C],Baltimore,Maryland,Jul.22-24,1991,771-776．
[167] C.Druet,D.Ernst and L.Wehenkel.Application of reinforcement learning to electrical power system closed-loop emergency control[A].Proceedings of Principles of Data Mining and Knowledge Discovery:The 4th European Conference,PKDD 2000[C],Lyon,France,Sep.,2000,1910:86-95．
[168] P.S.Braga.A topological reinforcement learning agent for navigation[J].Neural Computation and Applications,2003,(12) :220-236．
[169] P.K.Leslie,L.L.Michael and W.M.Andrew.Reinforcement learning:A survey[J].Journal of Artificial Intelligence Research,1996,(4) :237-285．
[170] D.B.Gu,H.S.Hu and L.Spacek.Learning fuzzy logic controller for reactive robot behaviours[A].Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics[C],Kobe,Japan,Jul.20-24,2003,1:46-51．
[171] C.Remi.Feedforward neural networks in reinforcement learning applied to high-dimensional motor control[A].Proceedings of Algorithmic Learning Theory:13th International Conference,ALT 2002[C],Lubeck,Germany,Nov.24-26,2002,403-413．
[172] C.Anderson,D.Hittle,A.Katz and R.Kretchmar.Synthesis of reinforcement learning,neural networks,and PI control applied to a simulated heating coil[J].Journal of Artificial Intelligence in Engineering,1997,11(4) :423-431．
[173] S. Norihisa, A. Masaharu and K. Makoto. Control of associative chaotic neural networks using reinforcement learning [A]. Proceedings of Advances in Neural Networks - ISNN 2004: International Symposium on Neural Networks [C], Dalian, China, Aug., 2004, 395-400.
[174] Y. Cang, H. C. Y. Nelson and D. W. Wang. A fuzzy controller with supervised learning assisted reinforcement learning algorithm for obstacle avoidance [J]. IEEE Transactions on Systems, Man, and Cybernetics, 2003, 33 (1): 17-27.
[175] A. O. Esogbue, W. E. Heames and Q. Song. A reinforcement learning fuzzy controller for set-point regulator problems [A]. Proceedings of the IEEE 5th International Fuzzy Systems Conference [C], New Orleans, LA, Sep. 8-11, 1996, 2136-2142.
[176] D. Dasgupta. Artificial immune systems and their applications [M]. Springer-Verlag. Inc. 1999.
[177] N. D. C. Leandro and T. Jonathan. Artificial immune system: A new computational intelligence approach [M]. Springer-Verlag. Inc., 2002.
[178] 蔡美英．医学免疫学[M]．北京：科学出版社，2002．
[179] Z. -Q. Qi and S. -M. Song. A novel immune feedback control algorithm and its applications [A]. Proceedings of Genetic and Evolutionary Computation Conference-GECCO 2004 [C], Seattle, WA, USA, Jun. 26-30, 2004, 318-320.
[180] K. Motohiro, S. Minoru and T. Kazuhiko. Adaptive learning method of network controller using an immune feedback [A]. Proceedings of the 1999 IEEE/ASME International Conference Advanced Intelligent Mechatronics [C], Atlanta, USA, Sep. 19-23, 1999, 641-646.
[181] K. Takashi and T. Yamada. Application of an immune feedback mechanism to control system [J]. JSME International Journal, Series C, 1998, 41(2): 184-191.
[182] D. H. Kim. PID controller tuning of a boiler control system using immune algorithm typed neural network [A]. Proceedings of Computational Science-ICCS 2004: 4th International Conference [C], Krakow, Poland, Jun. 6-9, 2004, 695-698.
[183] D. H. Kim and J. I. Park. Intelligent PID control by immune algorithms based fuzzy rule auto-tuning [J]. Lecture Notes in Computer Science, 2003, 2715: 474-482.
[184] A. Naim and A. H. Sadeq. Genetic design of fuzzy mapped PID controller for non-linear plants [J]. Information and Technology Journal, 2004, 3(1): 44-48.
[185] B. Y. Kim, G. J. Nam, H. S. Ryu and J. W. Lee. Optimization of filling process in RTM using genetic algorithm [J]. Korea-Australia Rheology Journal, 2000, 12(1): 83-92.
[186] M. I. Jordan and C. M. Bishop. Neural networks [J]. ACM Computing Surveys, 1996, 28(1): 73-75.
[187] C. M. Ennett, M. Frize and C. R. Walker. Influence of missing values on artificial neural network performance [A]. Proceedings of Medinfo 2001 [C], London, UK, Sep. 2-5, 2001, 10(1): 449-53.
[188] X. Yao and Y. Liu. Making use of population information in evolutionary artificial neural networks [J]. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 1998, 28(3): 417-425.
[189] X. Yao and Y. Liu. Towards designing artificial neural networks by evolution [J]. Applied Mathematics and Computation, 1998, 91(1): 83-90.
[190] J. C. Tay and A. Jhavar. CAFISS: a complex adaptive framework for immune system simulation [A]. Proceeding of 2005 ACM Symposium on Applied Computing (SAC) [C], Santa Fe, New Mexico, USA, Mar. 13-17, 2005, 158-164.
[191] S. Usan, A. C. John, C. G. Johnson, et al. Artificial immune systems and the grand challenge for non-classical computation [A]. Proceedings of the 2003 International Conference on Artificial Immune Systems [C], Lecture Notes in Computer Science, 2003, 2787: 204-216.
[192] S. Stepney, R. Smith, J. Timmis and A. Tyrrell. Towards a conceptual framework for artificial Immune Systems [A]. Proceedings of Third International Conference on Artificial Immune Systems [C], Lecture Notes in Computer Science, 2004, 3239: 3-64.
[193] J. C. Wommack, A. Salinas, R. H. Melloni Jr and Y. Delville. Behavioural and neuroendocrine adaptations to repeated stress during puberty in male golden hamsters [J]. Journal of Neuroendocrinology, 2004, 16 (9): 767-775.
[194] P. Haake, M. Schedlowski, M. S. Exton, et al. Acute neuroendocrine response to sexual stimulation in sexual offenders [J]. The Canadian Journal of Psychiatry, 2003, 48(4): 265-271.
[195] J. K. Payne. A neuroendocrine-based regulatory fatigue model [J]. Biological Research for Nursing, 2004, 6(2): 141-150.
[196] S. Cayzer and U. Aickelin. A recommender system based on the immune network[A]. Proceedings of 2002 Congress on Evolutionary Computation (CEC) [C], Honolulu, USA, May 12-17, 2002, 807-813.
[197] K. Motohiro, S. Minoru and T. Kazuhiko. Adaptive learning method of network controller using an immune feedback [A]. Proceedings of the 1999 IEEE/ASME International Conference Advanced Intelligent Mechatronics[C], Atlanta, USA, Sep. 19-23, 1999, 641-646.
[198] B. Liu, L. -H. Ren and Y. S. Ding. A novel intelligent controller based on modulation of neuroendocrine system [A]. Proceedings of the 2nd International Symposium on Neural Networks (ISNN 2005) [C], Lecture Notes in Computer Science, 2005, 3498(3): 119-124.
[199] B. Salemi, W. Shen and P. Will. Hormone controlled metamorphic robots [A]. Proceedings of the IEEE International conference on Robotics & Automation 2001[C], Seoul, Korea, 2001, 4194-4199.
[200] W. -M. Shen, B. Salemi and P. Will. Hormones for self-reconfigurable robots [A]. Proceedings of the International conference on Intelligent Autonomous Systems (IAS-6) [C], Venice, Italy, 2000, 918-925.
[201] 陈国初．微粒群优化算法及其在软测量建模中的应用[D]．华东理工大学博士学位论文，2006，5．
[202] L. G. Bushnell. Networks and control [J]. IEEE Control System Magazine, 2001, 21(1): 22-23.
[203] F. -L. Lian. Network design consideration for distributed control system [J]. IEEE Transactions on Control System Technology, 2002, 10(2): 297-307.
[204] G. A. Mintchell, C. Huitema. Ethernet's in control [J]. Control Engineering, 2000, 47(5): 46-54.
[205] S. Vitturi. On the use of Ethernet at low level of factory communication systems[J]. Computer Standards and Interfaces, 2001, (4): 267-277.
[206] F. P. Maturanaa, P. Tichy, P. Slechta, et al. Distributed multi-Agent architecture for automation systems [J]. Expert Systems with Applications, 2004, 26: 49-56.
[207] E. Camponogara and S. Talukdar. Designing communication networks for distributed control agents [J]. European Journal of Operational Research, 2004, 153: 544-563.
[208] R. Vadigepalli. Local intelligent control in biological systems and industrial process [EB/OL]. http://cdtower.hdpu.edu.cn/newwww/default.htm, Mar. 14, 2004
[209] 李力雄．基于网络整定的控制系统中网络诱导延时的分析及解决方法研究[D]．上海大学博士学位论文，2005，5．
[210] N. B. Almutairi, M. Y. Chow and Y. Tipsuwan. Network-based controlled DC motor with Fuzzy compensation [A]. Proceeding of the 27th Annual Conference of the IEEE Industrial Electronics Society [C], Nov. 29- Dec. 2, 2001, 3: 1844-1849.
[211] 方崇智，萧德云．过程辨识[M]．北京：清华大学出版社，1988．
[212] 王耀南．智能控制系统 模糊逻辑 专家系统 神经网络控制[M]．长沙：湖南大学出版社，1996．
[213] 金振林，高峰，李金良．并联3—2—1结构新型操作手及其承载能力分析[J]．中国机械工程，2002，13(2)：105-108．
[214] Attila L. Bencsik. Appropriate mathematical model of DC servo motors applied in SCARA robots [J]. Acta Polytechnica Hungarica, 2004, 1(2): 99-111.
[215] J. Santana, J. L. Naredo, F. Sandoval, I. Grout and O. J. Argueta. Simulation and constr-uction of a speed control for a DC series motor [J]. Mechatronics, 2002, 12(9-10): 1145-1156.
[216] A. Sevinc. A full adaptive observer for DC servo motors [J]. Turk J Elec Engin, 2003, 11(2): 117-130.
[217] M. J. Burridge and Z. -H. Qu. An improved nonlinear control design for series DC motors [J]. Computers & Electrical Engineering, 2003, 29(2): 273-288.
[218] J. Jugo. Real-time control of a DC motor using scilab and RTAI [EB/OL], http://scilabsoft.inria.fr/events/scilab2004/final_paper/9-JosuJugo_scilabRTA1.pdf, 2006, May 4.
[219] W. L. Xu and J. D. Han. Joint acceleration feedback control for robots: analysis, sensing and experiments [J]. Robotics and Computer-Integrated Manufacturing, 2000, 16(5): 307-320.