锚泊移位型工程船舶系统建模与控制研究
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摘要
长江航道整治工程中,使用了大量适应施工需求的专用工程船舶如挖泥船、铺排船、抛石船等,这些工程船一般为非自航锚泊型船舶,而且大多数是由其他船舶改装而来,自动化作业程度普遍比较低。在目前市场经济的大背景下,拥有高自动化程度的工程船就意味着效益和成绩,因此研究工程船锚泊移位自动控制系统,提高工程船施工作业精度、速度,有较大的实际应用价值。
本文首先研究了锚泊移位型船舶的系统建模问题。在已有文献研究的基础上,借鉴常规船舶模型分析方法,分析该类型船舶的运动机理和特点,推导了锚泊船二维和三维的线性运动模型。同时针对该类型船舶的特点和工程应用要求,基于神经网络辨识方法,提出了一种面向锚泊移位型工程船舶的系统辨识模型。以一类典型的锚泊移位工程船——软体铺排船为例,依托工程实践采集的实验数据对神经网络模型参数进行学习训练,得到铺排船纵向位移模型。
然后针对建模和控制的优化问题,本文分析研究了量子粒子群算法(Quantum-behaved Paticle Swarm Optimization,QPSO)。作为QPSO算法中唯一的参数,论文从问题依赖性、种群规模等多方面对收缩扩张系数β进行了大量的实验分析,根据实验结果得到该参数选取的指导性准则。为提高收敛速度,论文提出了一种改进的量子粒子群算法。通过仿真实验对比分析,该改进算法的收敛速度相对标准的QPSO算法有显著的提高。利用改进的QPSO算法对铺排船纵向位移模型参数进行了优化训练,优化后的模型作为自动控制系统的设计和仿真的对象模型。
基于模糊逻辑设计了工程船航迹保持控制系统,并利用改进的QSPO算法,对模糊控制器的模糊规则和隶属函数进行了优化设计。系统仿真实验结果表明,粒子群优化算法应用于模糊控制器的参数设计是可行和有效的,控制系统的动静态特性指标均能满足工程实践的要求。
本文提出了一种基于自适应神经-模糊推理系统(Adaptive Neuro-Fuzzy Inference System, ANFIS)的工程船自适应控制器网络结构,使控制器参数可随环境因素变化以适应不同的施工任务。采用改进的QPSO算法对自适应航迹保持控制网络的前后件参数进行了优化设计。与基于Fuzzy的航迹保持控制器的对比仿真实验结果表明,基于ANFIS的自适应航迹保持控制器的控制效果在动、静态性能均有提高。
本文提出基于ANFIS的航迹航向多变量自适应控制器网络模型,网络模型采用子网络并行学习模式,利用多个粒子群对应各子网络参数,使得网络优化训练过程简单、快速。结合工程船施工实际状况,选取典型实例对该自适应控制系统进行了仿真实验,实验结果显示该控制网络能够满足工程船多目标控制要求。
结合实际工程项目,将基于模糊逻辑和QPSO优化算法的航迹保持控制系统应用于软体铺排船控制系统中,同时综合应用PLC控制网络技术、GPS定位技术、多传感器信息融合技术、现场总线技术等先进的技术与手段,开发研制了一套软体铺排船作业综合自动监控系统。实船应用数据充分反映了本文探讨的工程船舶锚泊移位智能控制系统的有效性,对于同型工程船舶自动化水平的提高提供了一套切实可行的实施方案。其研究成果可推广应用到类似工程船舶的自动作业系统中,对相关领域的研究也有一定的参考价值。
最后,对全文进行了总结,并指出了在今后工作中需要进一步深入探讨的问题。
A large number of project vessels, such as hydraulic dredge, geotextiles-laying vessel, stone dumper, are used in regulation works for Yangze River. These project vessels usually belong to a kind of non self-propelled mooring shifting vessels. Remodeled from other common ship, these vessels generally have the poor degree of automation. Under the background of market economy, high-automated project vessels will product more economic benefit. Therefore, researching automated control system of project vessels is beneficial to precision of construction operation and economic benefit.
Firstly,the dissertation has studied this type vessels' system modeling. A lot of previous literatures were analyzed in detail. A linear two and three-dimensional moving model of mooring shifting project vessel was proposed,profit from the conventional ships model analysis method to analysis this vessels' moving mechanism and characteristic. Face to the this project vessel, the dissertation proposed a system identification plan based on the neural network. Take the geotextiles-laying vessel as example, parameters of the neural network model were training using the empirical datum, gathering from the engineering project. Then the displacement model had been established.
Then in view of modelling and controllor optimized question, this dissertation Analysises the QPSO (quantum-behaved paticle swarm optimization) algorithm. The contraction expansion coefficientβis the only parameter of QPSO algorithm.Sufficient experiment is done onβfrom different aspects, including the problem dependence and the swarm size. According to the experimental result, it obtains the selection guidance criterion of the parameter. In order to increase efficiency of convergence, it proposed a new improved QPSO optimization. Simulation experiments indicate that this algorithm improves convergence speed. The proposed algorithm was used to optimize the moving model of the mooring shifting project vessel, which was the object model for the control system design and simulation.
Based on fuzzy logic, a new track-keeping controller of mooring shifting project vessel was proposed. Particle swarm optimization algorithm was used to optimize fuzzy rules and membership. Simulation experiments indicate that particle swarm optimization algorithm is effectual for optimizing the parameters of fuzzy logic controller. The dynamic and the static performance indexes of the control system can meet the challenge of operating engineering.
A new adaptive controller network architecture of project vessel was proposed in the dissertation and this architecture was based on ANFIS (adaptive neuro-nuzzy inference system). Therefore, the parameters of controller can vary with working environment to accomplish different tasks. The improved QPSO algorithm was used to optimization design the antecedent and consequent parameters of adaptive track keeping control network. The result of simulation experiments shows that the dynamic and static performance of adaptive track keeping controller based on ANFIS, was improved when compared with the controller based on fuzzy logic.
The Multivariate adaptive controller network model was also purposed in the dissertation. The main learning pattern of this network model was sub-network parallel learning pattern. And the particle swarm was with corresponding sub-network parameters so that the network training processes would be easier and faster. Then, the simulation experiment of typical instance was chosen to analyze the effect of adaptive control system combined with the practical working situation of working ship. The experiment result shows that multivariate adaptive controller network was able to meet the multi-objective control requirement of project vessel.
The track-keeping controller based on the fuzzy logic and improved QPSO has been applied in the project item. An automatic monitoring system was developed to monitor the operation situation of geotextiles-laying vessel. The programmable logic controller, global positioning system, multi-sensor information fusion, fieldbus and other technologies have been applied in this system. Therefore, the intelligent optimized strategy, automatic control and centralized monitoring of geotextiles-laying arrangement operation have been realized. The level of automation and intelligence for operation has been enhanced.
The operation data of actual project vessel indicated that the mooring shifting intelligent control system of project vessel was effective, and this control system can also been applied to improve the automation of other ships. Its research results may promote apply in the similar project vessels' automatic work system, also has certain reference value to the related domain's research.
Finally, the summary of the dissertation and the future work to be investigated are presented.
本文首先研究了锚泊移位型船舶的系统建模问题。在已有文献研究的基础上,借鉴常规船舶模型分析方法,分析该类型船舶的运动机理和特点,推导了锚泊船二维和三维的线性运动模型。同时针对该类型船舶的特点和工程应用要求,基于神经网络辨识方法,提出了一种面向锚泊移位型工程船舶的系统辨识模型。以一类典型的锚泊移位工程船——软体铺排船为例,依托工程实践采集的实验数据对神经网络模型参数进行学习训练,得到铺排船纵向位移模型。
然后针对建模和控制的优化问题,本文分析研究了量子粒子群算法(Quantum-behaved Paticle Swarm Optimization,QPSO)。作为QPSO算法中唯一的参数,论文从问题依赖性、种群规模等多方面对收缩扩张系数β进行了大量的实验分析,根据实验结果得到该参数选取的指导性准则。为提高收敛速度,论文提出了一种改进的量子粒子群算法。通过仿真实验对比分析,该改进算法的收敛速度相对标准的QPSO算法有显著的提高。利用改进的QPSO算法对铺排船纵向位移模型参数进行了优化训练,优化后的模型作为自动控制系统的设计和仿真的对象模型。
基于模糊逻辑设计了工程船航迹保持控制系统,并利用改进的QSPO算法,对模糊控制器的模糊规则和隶属函数进行了优化设计。系统仿真实验结果表明,粒子群优化算法应用于模糊控制器的参数设计是可行和有效的,控制系统的动静态特性指标均能满足工程实践的要求。
本文提出了一种基于自适应神经-模糊推理系统(Adaptive Neuro-Fuzzy Inference System, ANFIS)的工程船自适应控制器网络结构,使控制器参数可随环境因素变化以适应不同的施工任务。采用改进的QPSO算法对自适应航迹保持控制网络的前后件参数进行了优化设计。与基于Fuzzy的航迹保持控制器的对比仿真实验结果表明,基于ANFIS的自适应航迹保持控制器的控制效果在动、静态性能均有提高。
本文提出基于ANFIS的航迹航向多变量自适应控制器网络模型,网络模型采用子网络并行学习模式,利用多个粒子群对应各子网络参数,使得网络优化训练过程简单、快速。结合工程船施工实际状况,选取典型实例对该自适应控制系统进行了仿真实验,实验结果显示该控制网络能够满足工程船多目标控制要求。
结合实际工程项目,将基于模糊逻辑和QPSO优化算法的航迹保持控制系统应用于软体铺排船控制系统中,同时综合应用PLC控制网络技术、GPS定位技术、多传感器信息融合技术、现场总线技术等先进的技术与手段,开发研制了一套软体铺排船作业综合自动监控系统。实船应用数据充分反映了本文探讨的工程船舶锚泊移位智能控制系统的有效性,对于同型工程船舶自动化水平的提高提供了一套切实可行的实施方案。其研究成果可推广应用到类似工程船舶的自动作业系统中,对相关领域的研究也有一定的参考价值。
最后,对全文进行了总结,并指出了在今后工作中需要进一步深入探讨的问题。
A large number of project vessels, such as hydraulic dredge, geotextiles-laying vessel, stone dumper, are used in regulation works for Yangze River. These project vessels usually belong to a kind of non self-propelled mooring shifting vessels. Remodeled from other common ship, these vessels generally have the poor degree of automation. Under the background of market economy, high-automated project vessels will product more economic benefit. Therefore, researching automated control system of project vessels is beneficial to precision of construction operation and economic benefit.
Firstly,the dissertation has studied this type vessels' system modeling. A lot of previous literatures were analyzed in detail. A linear two and three-dimensional moving model of mooring shifting project vessel was proposed,profit from the conventional ships model analysis method to analysis this vessels' moving mechanism and characteristic. Face to the this project vessel, the dissertation proposed a system identification plan based on the neural network. Take the geotextiles-laying vessel as example, parameters of the neural network model were training using the empirical datum, gathering from the engineering project. Then the displacement model had been established.
Then in view of modelling and controllor optimized question, this dissertation Analysises the QPSO (quantum-behaved paticle swarm optimization) algorithm. The contraction expansion coefficientβis the only parameter of QPSO algorithm.Sufficient experiment is done onβfrom different aspects, including the problem dependence and the swarm size. According to the experimental result, it obtains the selection guidance criterion of the parameter. In order to increase efficiency of convergence, it proposed a new improved QPSO optimization. Simulation experiments indicate that this algorithm improves convergence speed. The proposed algorithm was used to optimize the moving model of the mooring shifting project vessel, which was the object model for the control system design and simulation.
Based on fuzzy logic, a new track-keeping controller of mooring shifting project vessel was proposed. Particle swarm optimization algorithm was used to optimize fuzzy rules and membership. Simulation experiments indicate that particle swarm optimization algorithm is effectual for optimizing the parameters of fuzzy logic controller. The dynamic and the static performance indexes of the control system can meet the challenge of operating engineering.
A new adaptive controller network architecture of project vessel was proposed in the dissertation and this architecture was based on ANFIS (adaptive neuro-nuzzy inference system). Therefore, the parameters of controller can vary with working environment to accomplish different tasks. The improved QPSO algorithm was used to optimization design the antecedent and consequent parameters of adaptive track keeping control network. The result of simulation experiments shows that the dynamic and static performance of adaptive track keeping controller based on ANFIS, was improved when compared with the controller based on fuzzy logic.
The Multivariate adaptive controller network model was also purposed in the dissertation. The main learning pattern of this network model was sub-network parallel learning pattern. And the particle swarm was with corresponding sub-network parameters so that the network training processes would be easier and faster. Then, the simulation experiment of typical instance was chosen to analyze the effect of adaptive control system combined with the practical working situation of working ship. The experiment result shows that multivariate adaptive controller network was able to meet the multi-objective control requirement of project vessel.
The track-keeping controller based on the fuzzy logic and improved QPSO has been applied in the project item. An automatic monitoring system was developed to monitor the operation situation of geotextiles-laying vessel. The programmable logic controller, global positioning system, multi-sensor information fusion, fieldbus and other technologies have been applied in this system. Therefore, the intelligent optimized strategy, automatic control and centralized monitoring of geotextiles-laying arrangement operation have been realized. The level of automation and intelligence for operation has been enhanced.
The operation data of actual project vessel indicated that the mooring shifting intelligent control system of project vessel was effective, and this control system can also been applied to improve the automation of other ships. Its research results may promote apply in the similar project vessels' automatic work system, also has certain reference value to the related domain's research.
Finally, the summary of the dissertation and the future work to be investigated are presented.
引文
[1]顾敏童,高捷,谭家华.软体排铺设工程船介绍.造船技术,2000,(5)：34-36
[2]郭晓浩.长江铺排船的开发建设.船海工程,2003,(1)：1-6
[3]Balchen JG, Jenssen NA, Mathisen Eea. A dynamicpositioning system based on Kalman filtering and optimal control. Modeling, Identification and Control,1980, 1(3):135-163
[4]Deng ZL, Gu LL. The application of genetic theory in ship dynamic positioning system. Dyn Contin Discret Impuls Syst Ser B-Appl Algorithms,2006,13:1095-1098
[5]Fossen T. Guidance and control of ocean vehicles. New York:Wiley,1994
[6]Fung PTK, Grimble MJ.Dynamic ship positioning using a self-tuning Kalman filter. IEEE transactions on automatic control,1983,AC-28(3):339-349
[7]Katebi MR, Yamamoto I, Matsuura M, Grimble MJ, Hirayama H, Okamoto N. Robust dynamic ship positioning control system design and applications. Int J Robust Nonlinear Control,2001,11(13):1257-1284
[8]Nakamura M, Koterayama W, Kajiwara H, Hyakudome T. Model experiments on dynamic positioning system using gain scheduled controller. Int J Offshore Polar Eng, 2001,11(4):267-272
[9]Tannuri EA, Morishita HM. Experimental and numerical evaluation of a typical dynamic positioning system. Appl Ocean Res,2006,28(2):133-146
[10]付明玉,丁福光,边信黔,施小成.船舶动力定位系统滤波器的设计与研究.船舶工程,1996,(6)：43-48
[11]何黎明,田作华,施颂椒.动力定位船舶的非线性观测器设计.上海交通大学学报,2003,37(6)：964-969
[12]姜华,王建平.Kalman滤波技术在船舶动力定位技术中的应用.大连海事大学学报,1998,24(2)：54-57
[13]李家炜,秦再白,徐玉如等.动力定位系统的模糊自适应控制.海洋工程,1998,16(4)：27-33
[14]李家炜,徐玉如.船舶机动定位技术及其实现方法.海洋工程,2000,18(1)：29-32
[15]芮世民,朱继懋,黄根余.应用自适应模糊控制实施船舶动力定位.上海交通大学学报,2000,34(1)：56-59
[16]童进军,何黎明,田作华.船舶动力定位系统的数学模型.船舶工程,2002,(5)：27-29
[17]王宗义,肖坤,庞永杰,李殿璞.船舶动力定位的数学模型和滤波方式.哈尔滨工程大学学报,2002,23(4)：24-28
[18]王丹.工程船舶锚泊系统分析与应用[硕士学位论文].武汉：武汉理工大学,2007
[19]朱连宇.非自航工程船舶锚泊设备配置计算探讨.天津航海,2007,(3)：13-15
[20]余龙,谭家华.深水中悬链线锚泊系统设计研究进展.中国海洋平台,2004,(3)：24-29
[21]贾欣乐,杨盐生.船舶运动数学模型——机理建模与辨识建模.大连：大连海事大学出版社,1999.4
[22]薛梅,巩艳华.基于理性遗传算法的模糊系统辨识.计算机工程与应用,2008,(14)：73-75
[23]高桂革,曾宪文,顾幸生.基于小波逼近变换的非线性分布参数系统辨识.控制工程,2008,(4)：410-411
[24]徐小平,钱富才,刘丁,王峰.基于PSO算法的系统辨识方法.系统仿真学报,2008,(13)：3525-3528
[25]Bai EW, etal. Variable gain parameter estimation algorithms for fast tracking and smooth steady state. Automatica,2000,36:1001-1008
[26]万峰,孙优贤.非线性系统辨识中模糊模型参数收敛问题的进一步研究.自动化学报,2007,(1)：109-112
[27]朱世增,党选举.基于相关向量机的非线性动态系统辨识.计算机仿真,2008,(6)：103-107
[28]刘清.船舶操纵运动模糊神经网络控制系统研究武汉：武汉理工大学,2002
[29]孟浩.船舶航行的智能自适应控制研究哈尔滨：哈尔滨工程大学,2003
[30]姚淑慧,陈英士.自适应自动舵控制系统基本分析方法.大连海事大学学报,1998,24(1)：109-111
[31]杨盐生,余晓利,贾欣乐.船舶航向自适应鲁棒PID自动舵设计.武汉交通科技大学学报,1999,(6)：594-598
[32]杨盐生,贾欣乐.船舶航向的变结构控制自动舵设计.大连海事大学学报,1998,24(1)：14-18
[33]胡耀华,贾欣乐.广义预测控制应用于船舶航向和航迹保持.中国造船,1998,(1)：36-41
[34]胡耀华,贾欣乐.船舶运动的预测控制.大连海事大学学报,1998,24(1)：6-10
[35]Zadeh LA,陈国权译.模糊集合、语言变量及模糊逻辑.北京：科学出版社,1982
[36]Proce Sutton R.,A Int.,Mech. Design Study of a Self-Organizing Fuzzy Autopilot for Ship Control. Engns (ptI),1991,20(5):35-47
[37]Procyk T.J., Mamdani E.H. A Linguistic Self-organizing Process Controller. Automatica,1979,(15):15-30
[38]Jeffery R.Layne, Kevin M. Passino. Fuzzy Model Reference Learning Control orCargo Ship Steering. IEEE Trans Control Syst Mag,1993,13(6):23-34
[39]Michael. G. Parsons, Ailan. C. Chubb, Yusong Cao. An Assessment of Fuzzy Logic Vessel Path Control. IEEE J, Oceanic Eng,1995,20(4):276-284
[40]姚刚,汤天浩.一种基于PD与模糊复合控制的船舶航向变结构控制器.上海海运学院学报,2003,24(3)：200-204
[41]莫友声,孙军锋,李思恩.模糊自适应控制及其应用.上海交通大学学报,1996,30(11)：67-74
[42]胡江强,杨盐生,任俊生,李铁山.智能积分型的船舶航向自动舵.大连海事大学学报,2004,30(1)：14-17
[43]Endo M, Amerongen J.van, A.W.P. B.Applicability of Neural Networks to Ship Steering. CAMS'89:221-232
[44]Burns Roland S. The Use of Artificial Neural Networks for the Intelligent Optimal Control of Surface Ships. IEEE J,Oceanic Eng,1995,20(1):65-72
[45]Hearn G E.,Sen P, Zhang Y. Ship motion control by on-line trained neural networks. Proc Third International Conference on the Manoeuvring and Control of Marine Craft, Southampton, UK,1994:75-88
[46]Zhang Y, Hearn G E, Sen P. A neural network approach to ship track-keeping control. IEEE Journal of Oceanic Engineering,1996,21:513-527
[47]Zhang Y, Sen P, Hearn G E. A multivariable neural controller for automatic ship berthing. IEEE Control System Magazine,1997,17:31-45
[48]程启明,万德钧,陈雪丽.遗传学习算法的神经网络自适应船舶操纵控制系统研究.模式识别与人工智能,1998,11(3)：305-309
[49]程启明,万德钧,黄林.基于神经网络自适应PID控制的船舶操纵研究.电子测量与仪器学报,1998,12(2)：32-36
[50]鲛岛直人.荒天锚泊法に关する实验研究(第一报).日本航海学会论文集第22号,1960
[51]米田谨次郎.荒天锚泊法に关する实验研究.日本航海学会论文集第22号,1960
[52]Per.I. J. A Finite Element Model for Dynamic Analysis of Mooring Cables. Doctor Thesis of MIT,1976
[53]Fan J, Chen X, Ji C. Dynamic analysis of a tureet-moored tanker. China Ocean Engineering,2000,14(1):103-112
[54]John B, Ian M, Gill M. Multivariate Extreme Value Analysis of a Moored Semi-submersible. Marine Structure,1997,(10):443-463
[55]Juelv S. A three-dimensional dynamic analysis of a towed system. Ocean Eng,1983, 9(5):483-499
[56]王艳妮.海洋工程锚泊系统的分析研究[硕士学位论文].哈尔滨：哈尔滨工程大学,2006
[57]魏云雨.锚泊偏荡运动数学模型的研究[硕士学位论文].大连：大连理工大学,2001
[58]魏云雨,洪碧光,于洋.锚泊运动的数学模型.大连海事大学学报,2004,(3)：21-23
[59]Chai YT, Varyani KS, Barltrop NDP. Semi-analytical quasi-static formulation for three-dimensional partially grounded mooring system problems. Ocean Eng,2002, (29):627-649
[60]黄剑,朱克强.半潜式平台两种锚泊系统的静力分析与比较.华东船舶工业学院学报(自然科学版),2004,(3)：1-5
[61]孙宁松.海上移动式平台锚泊定位系统锚索链受力分析.中国海洋平台,2008,(2)：41-44
[62]马延德,孙德壮,王言英.浮式生产储油船锚泊定位性能计算.中国造船,2006,(4)：15-20
[63]Gaillard F, Terre T, Guillot A. Monitoring moored instrument motion by optimal estimation. Ocean Eng,2006,33(1):1-22
[64]GarzaRios LO, Bernitsas MM. Analytical expressions of the bifurcation boundaries for symmetric spread mooring systems. Appl Ocean Res,1995,17(6):325-341
[65]Leira BJ, Berntsen PIB, Aamo OM. Station-keeping of moored vessels by reliability-based optimization. Probab Eng Eng Mech,2008,23(2-3):246-253
[66]MC Kenna HAW, R.K. Synthetic fiber rope, properties and calculations relating to mooring systems. Deepwater Mooring & Drilling, ASME Trans, Ocean Engng Div, 1979, (7):189-203
[67]高捷,谭家华.用多目标决策方法优选浮式生产储油油轮定位方案.中国造船,2000,(2)：3-9
[68]茅听.新型船用锚泊定位装置的设计.机电设备,2007,(6)：7-10
[69]余龙,谭家华.深水二维对称式布置两成分锚泊线时域动力分析.江苏科技大学学报(自然科学版),2006,(4)：6-10
[70]冯刚,吴海帆,黄洪钟等.工程船锚泊移位系统的最优控制策略及拟静力分析.海洋工程,2001,19(4)：1-4
[71]陆昊.专家控制及其在工程船舶运动控制中的应用研究[硕士学位论文].武汉： 武汉理工大学,2006
[72]张媛.遗传算法优化模糊控制器的工程船舶控制方法的研究[硕士学位论文].武汉：武汉理工大学,2005
[73]张媛,李志俊.基于GA优化模糊控制规则的工程船舶控制方法.船海工程,2005,165(2)：28-31
[74]牛萍,王建国,潘学军,刘华毅.模糊神经网络系统在滩海铺管敷缆船中的应用设计.船舶工程,2000,(5)：28-32
[75]李冬荔,彪王,亮杨.船舶操纵线性水动力导数计算方法研究.中北大学学报(自然科学版),2008,29(6)：531-537
[76]刘应中,缪国平,李谊乐,刘滋源,刘和东.系泊系统动力分析的时域方法.上海交通大学学报,1997,31(11)：7-12
[77]石爱国,侯建国,蔡烽等.舰船风中运动仿真模型研究.中国航海学会海洋船舶驾驶专业委员会天气海洋与航海安全论文集,2000：151-159
[78]刘应中,缪国平.船舶在波浪上的运动理论.上海：上海交通大学出版社,1987
[79]Garza-Rios LO, Bernitsas MM, Nishimoto K. Slow motion dynamics of DICAS mooring systems under steady current,wind, and steady drift excitation. Offshore Engineering,1999,(1):47-61
[80]张纬康,杜度.系泊船舶动力学特性的计算机仿真研究.中国造船,2004,45(4)：1-10
[81]陈君义.船艺.北京：人民交通出版社,1992
[82]杜度,张宁,马骋,张纬康.系泊系统的时域仿真及其非线性动力学特性分析.船舶力学,2005,9(4)：37-45
[83]聂孟喜,王旭升,王晓明,张琳.风、浪、流联合作用下系统系泊力的时域计算方法.清华大学学报(自然科学版),2004,44(9)：1214-1217
[85]庄司邦昭.系留索ぃ动く张力の解析.东京商船大学研究报告,1986,37：25-26
[86]肖越,王言英.三维锚泊系统时域计算分析.船舶力学,2005,9(5)：8-16
[87]肖越.系泊系统时域非线性计算分析[博士学位论文].大连：大连理工大学,2005
[88]侯建军,东防,石爱国,尹建川.锚泊状态下锚链作用力的计算方法.大连海事大学学报,2005,31(4)：10-14
[89]翁维勤,孙洪程.过程控制系统及工程.北京：化学工业出版社,2002
[90]邵裕森,戴先中.过程控制工程.北京：机械工业出版社,2000
[91]王树青.工业过程控制工程.北京：化学工业出版社,2003
[92]Hecht NR. Theory of the backpropagation neural networks. Proceeding of the International Joint Conference on Neural Networks,1989,(1):593-611
[93]K.M. Hornik SaHW. Multiplayer feedforward networks are universal approximators. Neural network,1989, (2):359-366
[94]刘延年,冯纯伯.用神经网络进行非线性离散动态系统辨识的可行性.控制理论与应用,1994,11(4)：413-419
[95]王永骥,涂健.神经网络控制.北京：机械工业出版社,1998
[96]徐丽娜.神经网络控制.哈尔滨：哈尔滨工业大学出版社,1999
[97]Huang Z, Wang Y, Liu Q. Modeling Vessel Mooring Shift System Using Particle Swarm Optimization and the Neural Network Technology. Advances in systems science and applications,2007,7(2):246-252
[98]周开利,康耀红.神经网络模型及其MATLAB仿真程序设计.北京：清华大学出版社,2005.7
[99]Kennedy J, Eberhart R C, Shi Y. Swarm Intelligence. San Francisco:Morgan Kaufman Publishers,2001
[100]Sun J, Feng B, Xu W. Particle Swarm Optimization with Particles Having Quantum Behavior. Proceedings of the IEEE Congress on Evolutionary Computation,2004: 325-331
[101]Sun J, Xu W, Feng B. A global search strategy of quantum-behaved particle swarm optimization. Proceedings of IEEE Conference on Cybernetics and Intelligent Systems,2004:111-116
[102]Kennedy JE. Particle swarm optimization. Proc IEEE Int Conf on Neural Networks,1995:1942-1948
[103]Shi Y, Eberhart RC.A modified particle swarm optimizer. Proceedings of IEEE International Conference on Evolutionary Computation (CEC 1998), Piscataway, NJ, 1998:69-73
[104]Shi Y, Eberhart RC.Empirical study of particle swarm optimization. Proceedings of the World Multiconference on Systemics, Cybernetics and Informatics, Orlando, FL,2000:1945-1950
[105]Clerc M. The swarm and queen:towards a deterministic and adaptive particle optimization. Proceedings of the IEEE Congress on Evolutionary Computation, 1999:1951-1957
[106]Clerc MaK, J. The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions and on Evolutionary Computation,2002,6(1):58-73
[107]刘波,王凌,金以慧,黄德先.微粒群优化算法研究进展.化工自动化及仪表,2005,32(3)：1-6
[108]Trelea IC.The particle swarm optimization algorithm:convergence analysis and parameter selection. Information Processing Letters,2003,85(6):317-325
[109]Shi Y ERC. Fuzzy Adaptive Particle Swarm Optimization. Preceedings of the IEEECongress on Evolutionary Computation,2001:101-106
[110]Ratnaweera A HSK, Watson H C. Self-organizing Hierarchical Particle Swarm Optimizer with Time-varying Acceleration Coefficients. IEEE Transactions on Evolutionary Computation,2004,8(3):240-255
[111]王俊伟.粒子群优化算法的改进及应用[博士学位论文].沈阳：东北大学,2006
[112]刘宇,覃征,史哲文.简约粒子群优化算法.西安交通大学学报,2006,40(8)：883-887
[113]屈百达,焦竹青,徐保国.多量子粒子群协同优化算法研究.计算机工程与应用,2008,44(7)：72-74
[114]Lovbjerg M RTK, Krink T. Hybrid Particle Swarm Optimiserwith Breeding and Subpopulations. Proceedings of the Genetic and Evolutionary Computation Conference,2001:469-476
[115]van den Bergh F, P EA. A Cooperative Approach to Particle Swarm Optimization. IEEE Transactions on Evolutionary Computation,2004,8(3):225-239
[116]林川,冯全源.一种新的自适应粒子群优化算法.计算机工程,2008,34(7)：181-183
[117]Lovbjerg M KT. Extending Particle Swarms with Self-Organized Criticality. Proceedings of the Fourth Congress on Evolutionary Computation, Hobolulu, HI, USA,2002:1588-1593
[118]Xie X F, Zhang W J, L YZ. A Disssipative Particle Swarm Optimization. Proceedings of the IEEE Congress on Evolutionary Computation, Hobolulu, HI, USA,2002:1456-1461
[119]Kennedy J MR.. Population Structure and Particle Swarm Performance. Proceedings of the IEEE Congress on Evolutionary Computation, Hobolulu, HI, USA,2002:1671-1676
[120]Angeline PJ,.Using Selection to Improve Particle Swarm Optimization. IEEE International Conference on Evolutionary Computatrion, Anchorage, Alaska,1998: 84-89
[121]高鹰,谢胜利.基于模拟退火的粒子群优化算法.计算机工程与应用,2004,(1)：47-50
[122]吕振肃,候志荣.自适应变异的粒子群优化算法.电子学报,2004,32(3)：416-420
[123]林星,冯斌,孙俊.基于边界变异的量子粒子群优化算法.计算机工程,2008,34(12)：187-188+191
[124]林星,冯斌,孙俊.混沌量子粒子群优化算法.计算机工程与设计,2008,29(10)：2610-2612
[125]葛洪伟,靳文辉.变异量子粒子群优化算法在系统辨识中的应用.计算机工程与应用,2007,43(29)：222-2254+2238
[126]王璋,冯斌,孙俊.含维变异算子的量子粒子群算法.计算机工程与设计,2008,29(06)：1478-1481
[127]Eberhart RC, Shi Y. Comparing inertia weights and constriction factors in particle swarm optimization. Proceedings of the 2000 Congress on Evolutionary Computation,2000:84-88
[128]Kennedy J. Stereotyping:improving particle swarm performance with cluster analysis. Proceedings of the 2000 Congress on Evolutionary Computation,2000, (1502):1507-1512
[129]王俊年,申群太,陈湘州.自适应神经-模糊推理系统的混合协同微粒群算法进化设计.系统工程理论与实践,2006,(8)：48-54
[130]张航.非线性系统的神经模糊建模研究[硕士学位论文].大庆：大庆石油学院,2004
[131]张凯,钱锋,刘漫丹.模糊神经网络技术综述.信息与控制,2003,32(5)：431-435
[132]章庆生.长江岸堤防洪沉链排铺设工程船的研制.江苏船舶,2005,(3)：1-5
[133]Hydraulics WD. The Eastern Scheldt barrier:Environmentally friendly engineering? The II International Conference on Civil Engineering, Territory and the Environment of the Colegio de Ingenieros de Camines, Canales y Puertos, Santiago de Compostela, Spain,2004:1269-1282
[134]H. Hummel AWF, L. De Wolf and A. Meijboom. Mortality of intertidal benthic animals after a period of prolonged emersion. J Ep, Mrrr Bid Ed,1988, (121): 247-254
[135]唐军,刘正友.长江口深水航道治理工程船的设计.船舶设计通讯,2000,(3)：28-34
[136]曹根祥.长江口深水航道治理整治建筑物施工与关键技术研究[工程硕士学位论文].海河大学,2004
[137]陆梅兴.航道治理工程软体排铺设船关健技术的研究[硕士学位论文].上海：上海海运学院,2003
[138]邢晨.基于以太网和OPC技术的异构网络系统设计与实现[工学硕士学位论文]. 武汉：武汉理工大学,2006
[139]胡友健.全球定位系统原理与应用.北京：中国地质大学出版社,2003
[2]郭晓浩.长江铺排船的开发建设.船海工程,2003,(1)：1-6
[3]Balchen JG, Jenssen NA, Mathisen Eea. A dynamicpositioning system based on Kalman filtering and optimal control. Modeling, Identification and Control,1980, 1(3):135-163
[4]Deng ZL, Gu LL. The application of genetic theory in ship dynamic positioning system. Dyn Contin Discret Impuls Syst Ser B-Appl Algorithms,2006,13:1095-1098
[5]Fossen T. Guidance and control of ocean vehicles. New York:Wiley,1994
[6]Fung PTK, Grimble MJ.Dynamic ship positioning using a self-tuning Kalman filter. IEEE transactions on automatic control,1983,AC-28(3):339-349
[7]Katebi MR, Yamamoto I, Matsuura M, Grimble MJ, Hirayama H, Okamoto N. Robust dynamic ship positioning control system design and applications. Int J Robust Nonlinear Control,2001,11(13):1257-1284
[8]Nakamura M, Koterayama W, Kajiwara H, Hyakudome T. Model experiments on dynamic positioning system using gain scheduled controller. Int J Offshore Polar Eng, 2001,11(4):267-272
[9]Tannuri EA, Morishita HM. Experimental and numerical evaluation of a typical dynamic positioning system. Appl Ocean Res,2006,28(2):133-146
[10]付明玉,丁福光,边信黔,施小成.船舶动力定位系统滤波器的设计与研究.船舶工程,1996,(6)：43-48
[11]何黎明,田作华,施颂椒.动力定位船舶的非线性观测器设计.上海交通大学学报,2003,37(6)：964-969
[12]姜华,王建平.Kalman滤波技术在船舶动力定位技术中的应用.大连海事大学学报,1998,24(2)：54-57
[13]李家炜,秦再白,徐玉如等.动力定位系统的模糊自适应控制.海洋工程,1998,16(4)：27-33
[14]李家炜,徐玉如.船舶机动定位技术及其实现方法.海洋工程,2000,18(1)：29-32
[15]芮世民,朱继懋,黄根余.应用自适应模糊控制实施船舶动力定位.上海交通大学学报,2000,34(1)：56-59
[16]童进军,何黎明,田作华.船舶动力定位系统的数学模型.船舶工程,2002,(5)：27-29
[17]王宗义,肖坤,庞永杰,李殿璞.船舶动力定位的数学模型和滤波方式.哈尔滨工程大学学报,2002,23(4)：24-28
[18]王丹.工程船舶锚泊系统分析与应用[硕士学位论文].武汉：武汉理工大学,2007
[19]朱连宇.非自航工程船舶锚泊设备配置计算探讨.天津航海,2007,(3)：13-15
[20]余龙,谭家华.深水中悬链线锚泊系统设计研究进展.中国海洋平台,2004,(3)：24-29
[21]贾欣乐,杨盐生.船舶运动数学模型——机理建模与辨识建模.大连：大连海事大学出版社,1999.4
[22]薛梅,巩艳华.基于理性遗传算法的模糊系统辨识.计算机工程与应用,2008,(14)：73-75
[23]高桂革,曾宪文,顾幸生.基于小波逼近变换的非线性分布参数系统辨识.控制工程,2008,(4)：410-411
[24]徐小平,钱富才,刘丁,王峰.基于PSO算法的系统辨识方法.系统仿真学报,2008,(13)：3525-3528
[25]Bai EW, etal. Variable gain parameter estimation algorithms for fast tracking and smooth steady state. Automatica,2000,36:1001-1008
[26]万峰,孙优贤.非线性系统辨识中模糊模型参数收敛问题的进一步研究.自动化学报,2007,(1)：109-112
[27]朱世增,党选举.基于相关向量机的非线性动态系统辨识.计算机仿真,2008,(6)：103-107
[28]刘清.船舶操纵运动模糊神经网络控制系统研究武汉：武汉理工大学,2002
[29]孟浩.船舶航行的智能自适应控制研究哈尔滨：哈尔滨工程大学,2003
[30]姚淑慧,陈英士.自适应自动舵控制系统基本分析方法.大连海事大学学报,1998,24(1)：109-111
[31]杨盐生,余晓利,贾欣乐.船舶航向自适应鲁棒PID自动舵设计.武汉交通科技大学学报,1999,(6)：594-598
[32]杨盐生,贾欣乐.船舶航向的变结构控制自动舵设计.大连海事大学学报,1998,24(1)：14-18
[33]胡耀华,贾欣乐.广义预测控制应用于船舶航向和航迹保持.中国造船,1998,(1)：36-41
[34]胡耀华,贾欣乐.船舶运动的预测控制.大连海事大学学报,1998,24(1)：6-10
[35]Zadeh LA,陈国权译.模糊集合、语言变量及模糊逻辑.北京：科学出版社,1982
[36]Proce Sutton R.,A Int.,Mech. Design Study of a Self-Organizing Fuzzy Autopilot for Ship Control. Engns (ptI),1991,20(5):35-47
[37]Procyk T.J., Mamdani E.H. A Linguistic Self-organizing Process Controller. Automatica,1979,(15):15-30
[38]Jeffery R.Layne, Kevin M. Passino. Fuzzy Model Reference Learning Control orCargo Ship Steering. IEEE Trans Control Syst Mag,1993,13(6):23-34
[39]Michael. G. Parsons, Ailan. C. Chubb, Yusong Cao. An Assessment of Fuzzy Logic Vessel Path Control. IEEE J, Oceanic Eng,1995,20(4):276-284
[40]姚刚,汤天浩.一种基于PD与模糊复合控制的船舶航向变结构控制器.上海海运学院学报,2003,24(3)：200-204
[41]莫友声,孙军锋,李思恩.模糊自适应控制及其应用.上海交通大学学报,1996,30(11)：67-74
[42]胡江强,杨盐生,任俊生,李铁山.智能积分型的船舶航向自动舵.大连海事大学学报,2004,30(1)：14-17
[43]Endo M, Amerongen J.van, A.W.P. B.Applicability of Neural Networks to Ship Steering. CAMS'89:221-232
[44]Burns Roland S. The Use of Artificial Neural Networks for the Intelligent Optimal Control of Surface Ships. IEEE J,Oceanic Eng,1995,20(1):65-72
[45]Hearn G E.,Sen P, Zhang Y. Ship motion control by on-line trained neural networks. Proc Third International Conference on the Manoeuvring and Control of Marine Craft, Southampton, UK,1994:75-88
[46]Zhang Y, Hearn G E, Sen P. A neural network approach to ship track-keeping control. IEEE Journal of Oceanic Engineering,1996,21:513-527
[47]Zhang Y, Sen P, Hearn G E. A multivariable neural controller for automatic ship berthing. IEEE Control System Magazine,1997,17:31-45
[48]程启明,万德钧,陈雪丽.遗传学习算法的神经网络自适应船舶操纵控制系统研究.模式识别与人工智能,1998,11(3)：305-309
[49]程启明,万德钧,黄林.基于神经网络自适应PID控制的船舶操纵研究.电子测量与仪器学报,1998,12(2)：32-36
[50]鲛岛直人.荒天锚泊法に关する实验研究(第一报).日本航海学会论文集第22号,1960
[51]米田谨次郎.荒天锚泊法に关する实验研究.日本航海学会论文集第22号,1960
[52]Per.I. J. A Finite Element Model for Dynamic Analysis of Mooring Cables. Doctor Thesis of MIT,1976
[53]Fan J, Chen X, Ji C. Dynamic analysis of a tureet-moored tanker. China Ocean Engineering,2000,14(1):103-112
[54]John B, Ian M, Gill M. Multivariate Extreme Value Analysis of a Moored Semi-submersible. Marine Structure,1997,(10):443-463
[55]Juelv S. A three-dimensional dynamic analysis of a towed system. Ocean Eng,1983, 9(5):483-499
[56]王艳妮.海洋工程锚泊系统的分析研究[硕士学位论文].哈尔滨：哈尔滨工程大学,2006
[57]魏云雨.锚泊偏荡运动数学模型的研究[硕士学位论文].大连：大连理工大学,2001
[58]魏云雨,洪碧光,于洋.锚泊运动的数学模型.大连海事大学学报,2004,(3)：21-23
[59]Chai YT, Varyani KS, Barltrop NDP. Semi-analytical quasi-static formulation for three-dimensional partially grounded mooring system problems. Ocean Eng,2002, (29):627-649
[60]黄剑,朱克强.半潜式平台两种锚泊系统的静力分析与比较.华东船舶工业学院学报(自然科学版),2004,(3)：1-5
[61]孙宁松.海上移动式平台锚泊定位系统锚索链受力分析.中国海洋平台,2008,(2)：41-44
[62]马延德,孙德壮,王言英.浮式生产储油船锚泊定位性能计算.中国造船,2006,(4)：15-20
[63]Gaillard F, Terre T, Guillot A. Monitoring moored instrument motion by optimal estimation. Ocean Eng,2006,33(1):1-22
[64]GarzaRios LO, Bernitsas MM. Analytical expressions of the bifurcation boundaries for symmetric spread mooring systems. Appl Ocean Res,1995,17(6):325-341
[65]Leira BJ, Berntsen PIB, Aamo OM. Station-keeping of moored vessels by reliability-based optimization. Probab Eng Eng Mech,2008,23(2-3):246-253
[66]MC Kenna HAW, R.K. Synthetic fiber rope, properties and calculations relating to mooring systems. Deepwater Mooring & Drilling, ASME Trans, Ocean Engng Div, 1979, (7):189-203
[67]高捷,谭家华.用多目标决策方法优选浮式生产储油油轮定位方案.中国造船,2000,(2)：3-9
[68]茅听.新型船用锚泊定位装置的设计.机电设备,2007,(6)：7-10
[69]余龙,谭家华.深水二维对称式布置两成分锚泊线时域动力分析.江苏科技大学学报(自然科学版),2006,(4)：6-10
[70]冯刚,吴海帆,黄洪钟等.工程船锚泊移位系统的最优控制策略及拟静力分析.海洋工程,2001,19(4)：1-4
[71]陆昊.专家控制及其在工程船舶运动控制中的应用研究[硕士学位论文].武汉： 武汉理工大学,2006
[72]张媛.遗传算法优化模糊控制器的工程船舶控制方法的研究[硕士学位论文].武汉：武汉理工大学,2005
[73]张媛,李志俊.基于GA优化模糊控制规则的工程船舶控制方法.船海工程,2005,165(2)：28-31
[74]牛萍,王建国,潘学军,刘华毅.模糊神经网络系统在滩海铺管敷缆船中的应用设计.船舶工程,2000,(5)：28-32
[75]李冬荔,彪王,亮杨.船舶操纵线性水动力导数计算方法研究.中北大学学报(自然科学版),2008,29(6)：531-537
[76]刘应中,缪国平,李谊乐,刘滋源,刘和东.系泊系统动力分析的时域方法.上海交通大学学报,1997,31(11)：7-12
[77]石爱国,侯建国,蔡烽等.舰船风中运动仿真模型研究.中国航海学会海洋船舶驾驶专业委员会天气海洋与航海安全论文集,2000：151-159
[78]刘应中,缪国平.船舶在波浪上的运动理论.上海：上海交通大学出版社,1987
[79]Garza-Rios LO, Bernitsas MM, Nishimoto K. Slow motion dynamics of DICAS mooring systems under steady current,wind, and steady drift excitation. Offshore Engineering,1999,(1):47-61
[80]张纬康,杜度.系泊船舶动力学特性的计算机仿真研究.中国造船,2004,45(4)：1-10
[81]陈君义.船艺.北京：人民交通出版社,1992
[82]杜度,张宁,马骋,张纬康.系泊系统的时域仿真及其非线性动力学特性分析.船舶力学,2005,9(4)：37-45
[83]聂孟喜,王旭升,王晓明,张琳.风、浪、流联合作用下系统系泊力的时域计算方法.清华大学学报(自然科学版),2004,44(9)：1214-1217
[85]庄司邦昭.系留索ぃ动く张力の解析.东京商船大学研究报告,1986,37：25-26
[86]肖越,王言英.三维锚泊系统时域计算分析.船舶力学,2005,9(5)：8-16
[87]肖越.系泊系统时域非线性计算分析[博士学位论文].大连：大连理工大学,2005
[88]侯建军,东防,石爱国,尹建川.锚泊状态下锚链作用力的计算方法.大连海事大学学报,2005,31(4)：10-14
[89]翁维勤,孙洪程.过程控制系统及工程.北京：化学工业出版社,2002
[90]邵裕森,戴先中.过程控制工程.北京：机械工业出版社,2000
[91]王树青.工业过程控制工程.北京：化学工业出版社,2003
[92]Hecht NR. Theory of the backpropagation neural networks. Proceeding of the International Joint Conference on Neural Networks,1989,(1):593-611
[93]K.M. Hornik SaHW. Multiplayer feedforward networks are universal approximators. Neural network,1989, (2):359-366
[94]刘延年,冯纯伯.用神经网络进行非线性离散动态系统辨识的可行性.控制理论与应用,1994,11(4)：413-419
[95]王永骥,涂健.神经网络控制.北京：机械工业出版社,1998
[96]徐丽娜.神经网络控制.哈尔滨：哈尔滨工业大学出版社,1999
[97]Huang Z, Wang Y, Liu Q. Modeling Vessel Mooring Shift System Using Particle Swarm Optimization and the Neural Network Technology. Advances in systems science and applications,2007,7(2):246-252
[98]周开利,康耀红.神经网络模型及其MATLAB仿真程序设计.北京：清华大学出版社,2005.7
[99]Kennedy J, Eberhart R C, Shi Y. Swarm Intelligence. San Francisco:Morgan Kaufman Publishers,2001
[100]Sun J, Feng B, Xu W. Particle Swarm Optimization with Particles Having Quantum Behavior. Proceedings of the IEEE Congress on Evolutionary Computation,2004: 325-331
[101]Sun J, Xu W, Feng B. A global search strategy of quantum-behaved particle swarm optimization. Proceedings of IEEE Conference on Cybernetics and Intelligent Systems,2004:111-116
[102]Kennedy JE. Particle swarm optimization. Proc IEEE Int Conf on Neural Networks,1995:1942-1948
[103]Shi Y, Eberhart RC.A modified particle swarm optimizer. Proceedings of IEEE International Conference on Evolutionary Computation (CEC 1998), Piscataway, NJ, 1998:69-73
[104]Shi Y, Eberhart RC.Empirical study of particle swarm optimization. Proceedings of the World Multiconference on Systemics, Cybernetics and Informatics, Orlando, FL,2000:1945-1950
[105]Clerc M. The swarm and queen:towards a deterministic and adaptive particle optimization. Proceedings of the IEEE Congress on Evolutionary Computation, 1999:1951-1957
[106]Clerc MaK, J. The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions and on Evolutionary Computation,2002,6(1):58-73
[107]刘波,王凌,金以慧,黄德先.微粒群优化算法研究进展.化工自动化及仪表,2005,32(3)：1-6
[108]Trelea IC.The particle swarm optimization algorithm:convergence analysis and parameter selection. Information Processing Letters,2003,85(6):317-325
[109]Shi Y ERC. Fuzzy Adaptive Particle Swarm Optimization. Preceedings of the IEEECongress on Evolutionary Computation,2001:101-106
[110]Ratnaweera A HSK, Watson H C. Self-organizing Hierarchical Particle Swarm Optimizer with Time-varying Acceleration Coefficients. IEEE Transactions on Evolutionary Computation,2004,8(3):240-255
[111]王俊伟.粒子群优化算法的改进及应用[博士学位论文].沈阳：东北大学,2006
[112]刘宇,覃征,史哲文.简约粒子群优化算法.西安交通大学学报,2006,40(8)：883-887
[113]屈百达,焦竹青,徐保国.多量子粒子群协同优化算法研究.计算机工程与应用,2008,44(7)：72-74
[114]Lovbjerg M RTK, Krink T. Hybrid Particle Swarm Optimiserwith Breeding and Subpopulations. Proceedings of the Genetic and Evolutionary Computation Conference,2001:469-476
[115]van den Bergh F, P EA. A Cooperative Approach to Particle Swarm Optimization. IEEE Transactions on Evolutionary Computation,2004,8(3):225-239
[116]林川,冯全源.一种新的自适应粒子群优化算法.计算机工程,2008,34(7)：181-183
[117]Lovbjerg M KT. Extending Particle Swarms with Self-Organized Criticality. Proceedings of the Fourth Congress on Evolutionary Computation, Hobolulu, HI, USA,2002:1588-1593
[118]Xie X F, Zhang W J, L YZ. A Disssipative Particle Swarm Optimization. Proceedings of the IEEE Congress on Evolutionary Computation, Hobolulu, HI, USA,2002:1456-1461
[119]Kennedy J MR.. Population Structure and Particle Swarm Performance. Proceedings of the IEEE Congress on Evolutionary Computation, Hobolulu, HI, USA,2002:1671-1676
[120]Angeline PJ,.Using Selection to Improve Particle Swarm Optimization. IEEE International Conference on Evolutionary Computatrion, Anchorage, Alaska,1998: 84-89
[121]高鹰,谢胜利.基于模拟退火的粒子群优化算法.计算机工程与应用,2004,(1)：47-50
[122]吕振肃,候志荣.自适应变异的粒子群优化算法.电子学报,2004,32(3)：416-420
[123]林星,冯斌,孙俊.基于边界变异的量子粒子群优化算法.计算机工程,2008,34(12)：187-188+191
[124]林星,冯斌,孙俊.混沌量子粒子群优化算法.计算机工程与设计,2008,29(10)：2610-2612
[125]葛洪伟,靳文辉.变异量子粒子群优化算法在系统辨识中的应用.计算机工程与应用,2007,43(29)：222-2254+2238
[126]王璋,冯斌,孙俊.含维变异算子的量子粒子群算法.计算机工程与设计,2008,29(06)：1478-1481
[127]Eberhart RC, Shi Y. Comparing inertia weights and constriction factors in particle swarm optimization. Proceedings of the 2000 Congress on Evolutionary Computation,2000:84-88
[128]Kennedy J. Stereotyping:improving particle swarm performance with cluster analysis. Proceedings of the 2000 Congress on Evolutionary Computation,2000, (1502):1507-1512
[129]王俊年,申群太,陈湘州.自适应神经-模糊推理系统的混合协同微粒群算法进化设计.系统工程理论与实践,2006,(8)：48-54
[130]张航.非线性系统的神经模糊建模研究[硕士学位论文].大庆：大庆石油学院,2004
[131]张凯,钱锋,刘漫丹.模糊神经网络技术综述.信息与控制,2003,32(5)：431-435
[132]章庆生.长江岸堤防洪沉链排铺设工程船的研制.江苏船舶,2005,(3)：1-5
[133]Hydraulics WD. The Eastern Scheldt barrier:Environmentally friendly engineering? The II International Conference on Civil Engineering, Territory and the Environment of the Colegio de Ingenieros de Camines, Canales y Puertos, Santiago de Compostela, Spain,2004:1269-1282
[134]H. Hummel AWF, L. De Wolf and A. Meijboom. Mortality of intertidal benthic animals after a period of prolonged emersion. J Ep, Mrrr Bid Ed,1988, (121): 247-254
[135]唐军,刘正友.长江口深水航道治理工程船的设计.船舶设计通讯,2000,(3)：28-34
[136]曹根祥.长江口深水航道治理整治建筑物施工与关键技术研究[工程硕士学位论文].海河大学,2004
[137]陆梅兴.航道治理工程软体排铺设船关健技术的研究[硕士学位论文].上海：上海海运学院,2003
[138]邢晨.基于以太网和OPC技术的异构网络系统设计与实现[工学硕士学位论文]. 武汉：武汉理工大学,2006
[139]胡友健.全球定位系统原理与应用.北京：中国地质大学出版社,2003