粒子群优化算法研究及其在船舶运动参数辨识中的应用
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摘要
在船舶运动控制领域,建立船舶运动数学模型有两个目的:建立船舶操纵模拟器,为研究闭环系统性能提供一个基本的仿真平台;直接为设计船舶运动控制器服务。通过理论计算或实验得到的水动力参数精度难以保证,因此,目前大多采用辨识的方法得到水动力参数。从算法执行过程来看,水动力参数辨识问题可以归结为优化搜索问题。
粒子群优化算法是20世纪90年代提出的一种群智能优化算法。其优越的问题求解模式在优化问题的求解中取得了极大成功,引起了相关领域学者的广泛关注。实践已经证明,粒子群优化算法能够很好的解决复杂非线性条件下的多约束优化问题。本文主要针对粒子群优化算法及其在船舶运动水动力参数辨识中的应用进行研究,论文主要研究工作如下:
1.概述了粒子群优化算法的基本原理,并对粒子群优化算法的拓扑结构进行了详细的分析,阐述了粒子群优化算法的拓扑结构对算法性能的影响,给出了不同结构的粒子群优化算法对标准函数在算法性能、收敛速度、达优率等方面的测试结果。
2.分析了简化粒子群优化算法的收敛特性,并通过大量实验,详细分析了粒子群优化算法中各参数对算法收敛精度、达优率等方面的影响,提出了一种利用一个顶层粒子群优化算法对底层用来优化函数的粒子群优化算法的参数进行优化选取的方法。算法仿真和性能对比试验结果表明,该方法能够方便有效的实现粒子群优化算法参数的优化选取,有利于粒子群优化算法的应用和推广。
3.基于提高算法寻优能力和算法寻优速度两方面考虑,分别设计了基于进化的粒子群优化算法和基于分阶段搜索的粒子群优化算法。基于进化的粒子群优化算法在标准粒子群优化算法的基础上引入一种进化策略,增加粒子的多样性。在算法迭代寻优的过程中,通过对群体中的粒子进行选择、变异等进化操作,构造进化粒子群优化算法,提高算法的全局搜索能力。基于分阶段搜索的粒子群优化算法是利用粒子群搜索过程中参数收敛特性,对待辨识参数进行分组,采用分阶段搜索的方式进行辨识。求解结果表明,该算法能够快速的辨识各参数,验证了算法的有效性,该算法尤其适合高维数的复杂系统辨识。
4.对船舶纵向运动参数辨识问题进行了描述,分析了对船舶纵向运动参数辨识所要考虑的各方面因素。对观测数据的特性进行了分析,并给出了数据预处理的方法,另外,还设计了两种不同的输入参数建模方式,并分别进行了仿真。基于模糊CMAC神经网络的有关理论,以及通过切片理论计算和水池实验获得的数据,建立了任意航向、航速和海情的自适应变化的非线性参数智能化模型,为水动力参数辨识提供了有效的搜索区间。为了考察算法对噪声的适应能力,在有噪声干扰和没噪声干扰两种情况下都做了仿真,并进行了对比。仿真实验证明,基于改进的粒子群优化算法能够正确的辨识船舶纵向运动水动力参数,为船舶水动力参数辨识提供了一种新的解决方案。
5.对船舶横向运动参数辨识问题进行了描述,分析了船舶横向运动参数辨识的难点和解决方案。针对船舶横向水动力参数多、参数之间耦合度高的特点,提出了一种计算参数敏感性系数的方法,并依据敏感性系数对参数进行了分类,采用分阶段的方法对参数进行辨识。仿真结果表明,该方法能够正确有效的对船舶横向运动水动力参数进行辨识。
In the field of ship motion control, there are two purposes to establish the ship motion model. One is to establish a ship maneuvering simulator, providing a basic simulation platform for the study about close-loop system. The other is to service the design of ship motion controller. It is difficult to guarantee the accuracy of hydrodynamic parameters obtained through theoretical calculations or experiments, so many people obtain the hydrodynamic parameters by the identification algorithm. In the process of algorithm execution, this problem can be regarded as an optimization search.
Particle Swarm Optimization (PSO) algorithm is a swarm intelligence optimization algorithm proposed in the 1990s. The superiority of distributed solution model of problem in solving combinatorial optimization problem is great, and this causes large attention of the concerned fields. The practice has shown that, particle swarm optimization algorithm can well solve problems of multi-constraint optimization under complicated nonlinear conditions. This paper is mainly about particle swarm optimization algorithm and its application in the hydrodynamic parameter identification of ship motion, including:
1. The basic principle of the particle swarm optimization algorithm is summarized and the topology of PSO algorithm is analyzed in detail. Then, the influence of the topology to algorithm performance is analyzed. Last, some test results about PSO algorithm of different structures to the benchmark function in different aspects, such as algorithm performance, convergence rate, and rate of searching the optimization and so on are given.
2. The convergence of PSO algorithm is analyzed in detail. And the influence of parameters in PSO algorithm to some aspects, such as algorithm accuracy, rate of up to optimization and so on is analyzed in detail. Then a method which optimizes and selects parameters of the bottom PSO algorithm by using a top PSO algorithm is presented. The bottom PSO algorithm is applied to optimize function. The comparison of aloogorithm simulation and performance shows that this method can implement parameters optimization and the selection of PSO algorithm convenient effectively.
3. Considered the improvements of optimization capacity and speed, PSO algorithms based on evolutionary and phased search are designed. PSO algorithm based on evolutionary introduces an evolutionary strategy to increase the diversity of particles based on the standard PSO algorithm. In the process of algorithm iteration and optimization, the evolutionary PSO algorithm is constructed and the global search capabilityof the algorithm is improved through some operations on particles such as selection, mutation and so on. Making use of parameters convergence characteristics, PSO algorithm based on phased search divides parameters into groups, then identifies them. The solution makes clear that this algorithm can identify them quickly, and verify the effectiveness of the algorithm. The algorithm is particularly suitable for high-dimensional complex identification system.
4.The paper describes problem about parameter identification of ship vertical motion, and analyzes all factors to be taken into account. Then it analyzes the characteristics of these observed data, and gives a data pretreatment method. Moreover, two different modeling methods of parameters input are designed and simulated. Based on the relative theories in fuzzy CMAC neural network and the data from strip theory calculating and pool experiment, the adaptive nonlinear parameter model, which can adapt to any changes of course, speed and sea condition is built. The model provides the effective searching space of hydrodynamic parameters identification. In order to investigate the algorithm ability to adapt to noise, two simulations with and without noise are taken and compared. The result shows that the improved PSO can identify ship vertical motion hydrodynamic parameters accurately, and provides a new solution for hydrodynamic parameters identification.
5.The paper also describes problem about parameter identification of ship lateral motion, and analyses problems and solutions about this problem. It comes up with a method to calculate parameters sensitivity coefficients due to the characteristic of many ship lateral movement hydrodynamic parameters and high coupling, and classifies parameters according to sensitivity coefficients, then identifies them with phased method. The simulation results indicate that this method can carry on ship lateral motion hydrodynamic parameters identification correctly and effectively.
粒子群优化算法是20世纪90年代提出的一种群智能优化算法。其优越的问题求解模式在优化问题的求解中取得了极大成功,引起了相关领域学者的广泛关注。实践已经证明,粒子群优化算法能够很好的解决复杂非线性条件下的多约束优化问题。本文主要针对粒子群优化算法及其在船舶运动水动力参数辨识中的应用进行研究,论文主要研究工作如下:
1.概述了粒子群优化算法的基本原理,并对粒子群优化算法的拓扑结构进行了详细的分析,阐述了粒子群优化算法的拓扑结构对算法性能的影响,给出了不同结构的粒子群优化算法对标准函数在算法性能、收敛速度、达优率等方面的测试结果。
2.分析了简化粒子群优化算法的收敛特性,并通过大量实验,详细分析了粒子群优化算法中各参数对算法收敛精度、达优率等方面的影响,提出了一种利用一个顶层粒子群优化算法对底层用来优化函数的粒子群优化算法的参数进行优化选取的方法。算法仿真和性能对比试验结果表明,该方法能够方便有效的实现粒子群优化算法参数的优化选取,有利于粒子群优化算法的应用和推广。
3.基于提高算法寻优能力和算法寻优速度两方面考虑,分别设计了基于进化的粒子群优化算法和基于分阶段搜索的粒子群优化算法。基于进化的粒子群优化算法在标准粒子群优化算法的基础上引入一种进化策略,增加粒子的多样性。在算法迭代寻优的过程中,通过对群体中的粒子进行选择、变异等进化操作,构造进化粒子群优化算法,提高算法的全局搜索能力。基于分阶段搜索的粒子群优化算法是利用粒子群搜索过程中参数收敛特性,对待辨识参数进行分组,采用分阶段搜索的方式进行辨识。求解结果表明,该算法能够快速的辨识各参数,验证了算法的有效性,该算法尤其适合高维数的复杂系统辨识。
4.对船舶纵向运动参数辨识问题进行了描述,分析了对船舶纵向运动参数辨识所要考虑的各方面因素。对观测数据的特性进行了分析,并给出了数据预处理的方法,另外,还设计了两种不同的输入参数建模方式,并分别进行了仿真。基于模糊CMAC神经网络的有关理论,以及通过切片理论计算和水池实验获得的数据,建立了任意航向、航速和海情的自适应变化的非线性参数智能化模型,为水动力参数辨识提供了有效的搜索区间。为了考察算法对噪声的适应能力,在有噪声干扰和没噪声干扰两种情况下都做了仿真,并进行了对比。仿真实验证明,基于改进的粒子群优化算法能够正确的辨识船舶纵向运动水动力参数,为船舶水动力参数辨识提供了一种新的解决方案。
5.对船舶横向运动参数辨识问题进行了描述,分析了船舶横向运动参数辨识的难点和解决方案。针对船舶横向水动力参数多、参数之间耦合度高的特点,提出了一种计算参数敏感性系数的方法,并依据敏感性系数对参数进行了分类,采用分阶段的方法对参数进行辨识。仿真结果表明,该方法能够正确有效的对船舶横向运动水动力参数进行辨识。
In the field of ship motion control, there are two purposes to establish the ship motion model. One is to establish a ship maneuvering simulator, providing a basic simulation platform for the study about close-loop system. The other is to service the design of ship motion controller. It is difficult to guarantee the accuracy of hydrodynamic parameters obtained through theoretical calculations or experiments, so many people obtain the hydrodynamic parameters by the identification algorithm. In the process of algorithm execution, this problem can be regarded as an optimization search.
Particle Swarm Optimization (PSO) algorithm is a swarm intelligence optimization algorithm proposed in the 1990s. The superiority of distributed solution model of problem in solving combinatorial optimization problem is great, and this causes large attention of the concerned fields. The practice has shown that, particle swarm optimization algorithm can well solve problems of multi-constraint optimization under complicated nonlinear conditions. This paper is mainly about particle swarm optimization algorithm and its application in the hydrodynamic parameter identification of ship motion, including:
1. The basic principle of the particle swarm optimization algorithm is summarized and the topology of PSO algorithm is analyzed in detail. Then, the influence of the topology to algorithm performance is analyzed. Last, some test results about PSO algorithm of different structures to the benchmark function in different aspects, such as algorithm performance, convergence rate, and rate of searching the optimization and so on are given.
2. The convergence of PSO algorithm is analyzed in detail. And the influence of parameters in PSO algorithm to some aspects, such as algorithm accuracy, rate of up to optimization and so on is analyzed in detail. Then a method which optimizes and selects parameters of the bottom PSO algorithm by using a top PSO algorithm is presented. The bottom PSO algorithm is applied to optimize function. The comparison of aloogorithm simulation and performance shows that this method can implement parameters optimization and the selection of PSO algorithm convenient effectively.
3. Considered the improvements of optimization capacity and speed, PSO algorithms based on evolutionary and phased search are designed. PSO algorithm based on evolutionary introduces an evolutionary strategy to increase the diversity of particles based on the standard PSO algorithm. In the process of algorithm iteration and optimization, the evolutionary PSO algorithm is constructed and the global search capabilityof the algorithm is improved through some operations on particles such as selection, mutation and so on. Making use of parameters convergence characteristics, PSO algorithm based on phased search divides parameters into groups, then identifies them. The solution makes clear that this algorithm can identify them quickly, and verify the effectiveness of the algorithm. The algorithm is particularly suitable for high-dimensional complex identification system.
4.The paper describes problem about parameter identification of ship vertical motion, and analyzes all factors to be taken into account. Then it analyzes the characteristics of these observed data, and gives a data pretreatment method. Moreover, two different modeling methods of parameters input are designed and simulated. Based on the relative theories in fuzzy CMAC neural network and the data from strip theory calculating and pool experiment, the adaptive nonlinear parameter model, which can adapt to any changes of course, speed and sea condition is built. The model provides the effective searching space of hydrodynamic parameters identification. In order to investigate the algorithm ability to adapt to noise, two simulations with and without noise are taken and compared. The result shows that the improved PSO can identify ship vertical motion hydrodynamic parameters accurately, and provides a new solution for hydrodynamic parameters identification.
5.The paper also describes problem about parameter identification of ship lateral motion, and analyses problems and solutions about this problem. It comes up with a method to calculate parameters sensitivity coefficients due to the characteristic of many ship lateral movement hydrodynamic parameters and high coupling, and classifies parameters according to sensitivity coefficients, then identifies them with phased method. The simulation results indicate that this method can carry on ship lateral motion hydrodynamic parameters identification correctly and effectively.
引文
[1]刘应中,缪国平.船舶在波浪上的运动理论[M]. 1987: 151-173页
[2]贾欣乐,张显库.船舶运动智能控制与H∞鲁棒控制[M].大连:大连海事大学出版社出版.2002: 15-33页
[3]陶尧森.船舶耐波性[M].上海:上海交通大学出版社,1985: 52-83页
[4]陈虹丽,刘贵栋,赵希人等.船舶纵向运动水动力参数模型的建立及分析[J].仪器仪表学报. 2006, 27(6): 1120-1121页
[5] Kennedy J,Eberhart R C.Particle swarm optimization proc[A].In:IEEE Service Center ed.IEEE International Conference on Neural Networks[C],Perth,Australia,995.Piscataway: IEEE Press,1995: 1942-1948P
[6] Eberhart R, Kennedy J. A New Optimizer Using Particle Swarm Theory [A]. In: Proc of the Sixth International Symposium on Micro Machine and Human Science[C]. Nagoya, Japan, 1995: 39-43P
[7] Ozcan E, Mohan C k. Analysis of a simple particle swarm optimiaion system [J]. Intelligent Engineering Systems through Aritificial Neural Networks, 1998:253-258P
[8] Ozcan E, Mohan C k. Particle swarm optimization: Surfing the waves. Proceedings of the Congress on Evolutionary Computation, Piscataway[J]: IEEE Service Center, 1999:1939-1944P
[9] Clerc M,and Kennedy J. The Particle Swarm: ExPlosion, Stability, and Convergence in a ComPlex Space [J], IEEE Transaction On Evolutionary ComPutation, 2002, 6(l):58-73P
[10] F. van den Bergh. An Analysis of Particle Swarm Optimizers: [PHD Thesis].Department of Computer Science, University of Pretoria, South Africa, 2002.
[11]高尚,汤可宗等.粒子群优化算法收敛性分析[J].科学技术与工程. 2006, 6(12): 1625-1631P
[12] Shi Y, Eberhart R C. A modified particle swarm optimizer [A], IEEE Int. Conf. on Evolutionary Computation [C], Piscataway, NJ, IEEE Service Center, 1998: 69-73P
[13] Eberhart R C, Shi Y. ComParing inertia weigthts and constriction factors in particle swarm optimization[C]. Proeeedings of the IEEE Congress on Evolutionary ComPutation (CEC2000), SanDiego, CA.2000: 84-88P
[14] Shi Y, Eberhart R C. Parameter selection in particle swarm optimization [A], Proceedings of the Seventh Annual Conference on EvolutionaryProgramming[C], Washington DC, 1998: 591-600P
[15] Shi Y, Eberhart R C. Empitical study of particle swarm optimization [A], proceeding of the Congress on Evolutionary Computation[C], Washington DC, 1999: 1945-1950P
[16] Suganthan P N. Particle swarm optimizer with neighborhood operator [A]. Proc.of the Congress on Evolutionary Computation[C], Washington DC, 1999: 1958-1962P
[17] Shi Yuhui, Eberhart R C. Fuzzy adaptive particle swarm optimization [A], Proc. IEEE. Int Conf on Evolutionary Computation [C], Seoul, Korea, 2001: 101-106P
[18]王俊伟,汪定伟.粒子群优化算法中惯性权重的实验与分析[J].系统工程学报, 2005, 20(2): 194-198P
[19] I. C. Trelea. The particle swarm optimization algorithm: Convergence analysis and parameter selection [J]. Information Processing Letters, 2003, 85 (6): 317-325P
[20] M.Jiang, Y.P. Luo, S.Y. Yang. Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm [J]. Information Processing Letters. 2007, 102: 8-16P
[21] Nayan R. Samal, Amit Konar, Atulya Nagar. Stability Analysis and Parameter Selection of a Particle Swarm Optimizer in a Dynamic Environment[C]. Second UKSIM European Symposium on Computer Modeling and Simulation.2008: 21-27P
[22] Ratnaweera A, Halgamuge S, Watson H. Self-organizing hierarehical Particle swarm optimizer with time varying accelerating coeffieients[C]. IEEE Trans. Evol. ComPut, vol.8, Jun.2004: 240-255P
[23] Monson C K, Sepp K D. The Kalman Swarm- A New Approach to Particle Motion in Swarm Optimization[C]. Proceedings of the Genetic and Evolutionary Computation Conference. Springer, 2004: 140-150P
[24] Zielinski Karin, Laur Rainer. Adaptive parameter setting for a multi-objective particle swarm optimization algorithm[C]. 2007 IEEE Congress on Evolutionary ComPutation, CEC 2007: 3019-3026P
[25]王维博,林川,郑永康.粒子群优化算法中参数的实验与分析[J].西华大学学报自然科学版2008,27(1): 76-80页
[26]潘冠宇,刘大有,窦全胜,刘晓华. PSO方法的收敛性及基于微分演化的参数确定策略[J].吉林大学学报(工学版). 2007, 37(4): 842-845P
[27] Kennedy J. Small Worlds and Mega-minds: Effects of NeighborhoodTopology on Particle Swarm Performance[C]. In: The 1999 Congress on Evolutionary Computation. Piscataway, NJ: IEEE Press, 1999: 1931-1938P
[28] Kennedy J, R Mendes. Topological Structure and Particle Swarm Performance[C]. In: The 2002 Congress on Evolutionary Computation.Piscataway, NJ: IEEE Service Center, 2002: 1671-1676P
[29] Mendes R, Kennedy J. The Full Informed Particle Swarm: simpler, maybe better[C]. IEEE TRANSACTION ON EVOLUTIONARY COMPUTATION, 2004, 8(3): 204-210P
[30] Mendes R.Population Topologies and Their Influence in Particle Swarm Performance:[PHD Thesis]. Department of Information, University of Minho, Braga, Portugal. 2004.
[31] Angeline P J. Evolutionary optimization versus particle warm optimization: Philosophy and performance difference [A], Proc of the 7th Annual Conf on Evolutionary Programming [C], Gemany: Springer, 1998: 601-610P
[32] Riget J, Vesterstr?m J S. A diversity-guided particle swarm optimization-the ARPSO, EVALife Technical Report no.2002-02P
[33] L?vbjerg M, Krink T. Extending particle swarms with self-organized criticality [A], Proceedings of the Fourth Congress on evolutionary computation [C], Honolulu, HI USA, 2002, 2: 1588-1593P
[34] Krink T, Vesterstr?m J S, Riget J. Particle swarm optimization with spatial particle extension[A], Proceedings of the Fourth Congress on Evolutionary Computation[C], Honolulu, HI USA, 2002, 2:1474-1479P
[35] Angeline P J. Using Selection to Improve Particle Swarm Optimization [R], IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, 1998.
[36] Higashi N, Iba H. Particle Swarm Optimization with Gaussian Mutation[C]. In: The 2003 Congress on Evolutionary Computation. Piscataway, NJ: IEEE Press, 2003: 72-79P
[37] NING LI, YUAN-QING QIN, DE-BAO SUN, TONG ZOU. Particle Swarm Optimization with Mutation Operator[C]. In: The Third International Conference on Machine Learning and Cybernetics. Piscataway, NJ: IEEE Press, 2004: 2251-2256P
[38]吕振肃,侯志荣.自适应变异的粒子群优化算法[J].电子学报,2004,32(3):416-420页.
[39] Baskar S, Suganthan P. N. A Novel Concurrent Particle Swarm Optimization[C]. In: The 2004 Congress on Evolutionary Computation. Piscataway, NJ:IEEE Press, 2004:792-796P
[40] Shi Y, Krohling R. A. Co-evolutionary Particle Swarm Optimization to Solve Min-max Problems[C]. In: The 2002 Congress on Evolutionary Computation. Piscataway, NJ:IEEE Press,2002:1682-1687P
[41] Vesterstr?m J. S, Riget J and Krink T. Division of Labor in Particle Swarm Optimisation[C]. In: The 4thCongress on Evolutionary Computation. Piscataway, NJ: IEEE Press, 2002: 1570-1575P
[42] Janson S, Middendorf M. A Hierarchical Particle Swarm Optimizer[C]. In: The 2003 Congress on Evolutionary Computation. Piscataway, NJ: IEEE Press, 2003: 770-776P
[43]陈民铀,张聪誉,罗辞勇.自适应进化多目标粒子群优化算法[J].控制与决策, 2009,24(12): 1851-1855页
[44] L?vbjerg M, Rasmussen T. K, and Krink T. Hybrid Particle Swarm Optimizer with Breeding and Subpopulations[C]. In: Third Genetic and Evolutionary Computation Conference.Piscataway, NJ:IEEE Service Center, 2001: 469-476P
[45]胡广浩,毛志忠,何大阔.基于遗传和粒子群结合的文化算法[J].东北大学学报, 2009, 30(11): 1542-1545页
[46] Yang S,Wang, M and Jiao L.A Quantum Particle Swarm Optimization[C]. In: The 2004 Congress on Evolutionary Computation. Piscataway, NJ:IEEE Press, 2004:320-324P
[47] Zhisheng Zhang, Quantum-behaved particle swarm optimization algorithm for economic load dispatch of power system [J]. Expert Systems with Applications, 2010, 37(2): 1800-1803P
[48] Jun Sun,Bin Feng and Wenbo Xu.Particle Swarm Optimiztion with Particles Having Quantum Behavior.In:The 2004 Congress on Evolutionary Computation.Piscataway,NJ:IEEE Press, 2004: 325-331P
[49] Wang Xihuai, Li Junjun. Hybrid Particle Swarm Optimization with Simulated Annealing. In:The 3rd International Conference on Machine Learning and Cybernetics. Piscataway, NJ: IEEE Press, 2002: 2402-2405P
[50] Xie X, Zhang W and Yang Z. A Dissipative Particle Swarm Optimization [C]. In: The 2002 Congress on Evolutionary Computation. Piscataway, NJ: IEEE Press, 2002: 1456-1461P
[51] Xie X, Zhang W and Yang Z. Adaptive Particle Swarm Optimization on Individual Level [J]. In: International Conference on Signal Processing. Piscataway, NJ: IEEE Service Center, 1998: 1215-1218P
[52] Hui Liu, Zixing Cai, Yong Wang. Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization [J]. Applied Soft Computing. 10, (2010): 629-640P
[53] Nakano S, IshigameA, Yasuda K. Particle swam optimization based on the concept of Tabu search [C]. 2007 IEEE Congress on Evolutionary Computation, 2007: 3258-63P
[54] Bo Liu, Ling Wang, Yi-Hui Jin, Fang Tang, De-Xian Huang. Improved particle swarm optimization combined with chaos [J]. Chaos, Solitons and Fractals. 25, (2005): 1261-1271P
[55] P.S. Shelokar, Patrick Siarry, V.K. Jayaraman, B.D. Kulkarni. Particleswarm and ant colony algorithms hybridized for improved continuous optimization [J]. Applied Mathematics and Computation, 188, (2007): 129-142P
[56]吕强,刘士荣,邱雪娜.基于信息素机制的粒子群优化算法的设计与实现[J].自动化学报, 2009, 35 (11): 1410-1419页
[57]高鹰,谢胜利.免疫粒子群优化算法[J].计算机工程与应用, 2004, 40(6): 4-6页
[58]邹彤,李宁,孙德宝,岑翼刚.带阴性选择的粒子群优化算法[J].华中科技大学学报(自然科学版), 2006, 34(2):87-90页
[59] R.Brits, A. P. Engelbrecht and F.van den Bergh. A Niching Particle Swarm Optimizer[C]. In: The 4th Asia-Pacific Conference on Simulated Evolution and Learning. Piscataway, NJ: IEEE Press, 2002: 692-696P
[60]周殊,潘炜,罗斌.一种基于粒子群优化方法的改进量子遗传算法及应用[J].电子学报, 2006, 34(5): 897-901页
[61] ShengliSong, LiKong, Yong Gan, Rijian Su.Hybrid particle swarm cooperative optimization algorithm and its application to MBC in alumina production [J]. Progress in Natural Seience, Vol.18(11), 2008: 1423-1428P
[62] Dong qing, Wanga, Feng Ding. Extended stochastic gradient identification algorithms for Hammerstein- Wiener ARMAX systems [J]. Computers and Mathematics with Applications. 2008, 56: 3157-3164P
[63]徐小平,钱富才,王峰.一种辨识Wiener-Hammerstein模型的新方法[J].控制与决策. 2008, 23(8): 929-934页
[64]向微,陈宗海.基于模型描述的非线性系统辨识新方法[J].控制理论与应用.2007,24(1): 143-147页
[65] Hou Zhi-Xiang. Wiener model identification based on adaptive particle swarm optimization[C]. Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC.2008,vol(2):1041-1045P
[66] Wang Youbo, Wei Benzheng. A new particle swarm optimization based auto-tuning of PID controller [J]. International Journal of Control, Automation and Systems, 2009, 7(2): 273-280P
[67] Jalilvand, A., Kimiyaghalam, A., Ashouri, A.,Mahdavi, M. Advanced particle swarm optimization-based PID controller parameters tuning[C]. IEEE INMIC 2008. 12th IEEE International Multitopic Conference, 2008: 429-435P
[68]胡海兵,胡庆波,吕征宇.基于粒子群优化的PID伺服控制器设计[J].浙江大学学报(工学版), 2006, 40(12): 2144-2148页
[69] WUH, SunFC, Sun Z Q et al. Optimal Trajectory Planmng of a Flexible Dualarm Space Robot with Vibration Reduction [J]. Journal of Intelligent & RobotieSystems, 2004, 40(2): 147-163P
[70] Qing Z , Limin Q, Yingchun L et al. An improved particle swam optimization algorithm for vehicle routing problem with time windows[C]. 2006 IEEE Congress on Evolutionary Computation,CEC 2006, 2006: 1386-1390P
[71]刘海江,黄炜.基于粒子群优化算法的数控加工切削参数优化[J].同济大学学报(自然科学版), 36(6), 2008, 803-506页
[72] Yang L, Hu W W, Yang S Y et al. Application of Particle Swam Optimization in Multi-sensor Multi-target Traeking [C]. 1st International Symposium on Systems and Control in Aerospace and Astronautics, 2006: 715-719P
[73]胡炜薇,杨雷,杨萃元,廖艳苹.粒子群优化算法在多传感器多目标跟踪的应用[J].哈尔滨工程大学学报,28(1), 2007: 102-107页
[74]张文静,赵先章,台宪青.基于粒子群优化算法的火炮伺服系统摩擦参数辨识[J].清华大学学报(自然科学版), 2007, 47(s2): 1717-1720页
[75] Liu Y, Zhang H, et al, Particle Swarm Optimization for Fault State Power Supply Reliability Enhancement [C]. In: Proc of the IEEE International Conference on Intelligent Systems Application to Power Systems, Budapest, Hungary, 2001: 172-176[J]
[76] Yoshida H. AParticle swam optimization for reactive power and voltage control considering voltage security assessment [J]. Transactions of the Institute of Electrical Engineers of Japan, 1999, 119-B(12): 1462-1469P
[77]张凌杰,张国辉.电力系统经济负荷分配的混合粒子群优化算法.计算机工程与应用. 2009, 45(33): 202-205页
[78] Naka S. A hybrid particle swarm optimization for distribution state estimation [J]. IEEE Transactions on power Systems 2003, 18(1), 60-68P
[79]张智晟,董存,吴新振.基于量子粒子群优化算法的水电系统经济运行[J]. 2009, 33(18): 68-72页
[80] Gaing Z L.Particle Swarm Optimization to Solving the Economic Dispatch Considering the Generator Constrainis [J]. IEEE Transactions on Power Systems, 2003, 18(3): 1187-1195P
[81] Yoshida H,Kawata K,Fukuyama Y.et al.A Particle Swarm Optimization for Reactive Power and Voltage Control Considering Voltage Stability[C]. In:Proc of the International Conference on Intelligent System Application to Power Systems.Rio de Janeiro, Brazil, 1999: 1l7-121P
[82] Sensarma P S, Rahmani M, Carvalho A. A comprehensive method for optimal expansion planning using particle swarm optimization [C]. IEEE Power Engineering Society Winter Meeting, 2002, 2: 1317-1322P
[83]肖军,刘天琪,苏鹏.基于双种群粒子群优化算法的分时段电力系统无功优化[J].电网技术. 2009, 33(8): 72-77页
[84]陈华东,王树宗,王航宇.基于混合粒子群优化算法的多平台多武器火力分配研究[J].系统工程与电子技术, 2008, 30(5): 880-883页
[85] Xiangping Zeng,Yunlong Zhu, Lin Nan1, et al. Solving weapon-target assignment problem using discrete particle swarm optimization[C]. Sixth World Congress on Intelligent Control and Automation. 2006, 21-24P
[86]单敏瑜,刘以安,倪天权. QPSO在无人机侦察航路规划中的应用研究[J].计算机工程与设计. 2009,30(20): 4690-4692,4773页
[87] Ding Zhu, Ma Da-wei, Tang Ming-duan, Zhang Xue-feng. TSAPSO: a hybrid search algorithm of tabu search and annealing particle swarm optimization for weapon-target assignment [J]. Journal of System Simulation. 2006, 18(9): 2480-2483P
[88]孔国杰,张培林,徐龙堂,吴烽.一种新的火炮初速下降量预测模型.弹道学报. 2009, 21(3): 65-68页
[89]王国栋,李明,陈希成,范彦铭.基于改进的粒子群优化算法的无人作战飞机航路规划[J].航空计算技术. 2007,37(4): 9-13页
[90] Jun Sun, C. H. Lai, Wenbo Xu. Empirical study on stock market through ARCH model based on particle swarm optimization. DCABE 2006 Proceedings. 2006: 827-831P
[91]朱嘉瑜,叶海燕,高鹰.基于隐马尔可夫模型的股票价格预测组合模型[J].计算机工程与设计. 2009,30 (21): 4945-4938页
[92]周辉仁,郑丕谔,王嵩,刘春霞.基于粒子群优化算法的LS-SVM财务预警[J].计算机工程. 2009, 35(9): 280-282页
[93] Briza Antonio C, Naval Jr, Prospero C. Design of stock trading system for historical market data using multiobjective particle swarm optimization of technical indicators [CA]. 10th Annual Genetic and Evolutionary Computation Conference, 2008: 1871-1878P
[94]岑翼刚,秦元庆,孙德宝.粒子群优化算法在小波神经网络中的应用[J].系统仿真学报. 2004, 16(12): 2783-2785页
[95] EI-Telbany M. A., Konsowa H.G.,EI-Adawy M., Studying the predictability of neural network trained by particle swam optimization [J]. Journal of Engineering and Applied Scienee, 2006, 53(3): 377-390P
[96] Su Rijian, Kong Li, Song Shengli et al. A new ridgelet neural network training algorithm based on improved particle swarm optimization[C]. Proeeedings-Third International Conference on Natural Computation, ICNC 2007, 2007, vol.3: 411-415P
[97] CarvalhoMareio, LudermirTeresaB, Particle swam optimization of neural network architectures and weights [C]. Proeeedings-7th International Conference on Hybrid Intelligent Systems, HIS2007, 2007: 336-339P
[98] Ismail A, Engelbreeht A. Training Product units in feedforward neural networks using particle swarm optimization [C]. Proeeeding of the International Conference on Artificial intelligence. Durban, South Africa, 1999: 36-40P
[99]都延丽,吴庆宪,姜长生,周丽.改进协同微粒群优化的模糊神经网络控制系统设计[J].控制与决策. 2008, 23(12): 1328-1337页
[100] Eberhart R, Hu X H. Human tremor analysis using particle swam optimization[C]. Proeeedings of the 1999 Congress on Evolutionary Computation. Piscataway, NJ, USA: IEEE, 1999: 1927-1930P
[101]康琦.微粒群优化算法的研究与应用[D].上海:同济大学, 2005: 4-10页
[102]吴启迪,汪镭.智能蚁群算法及应用[M].上海:上海科技教育出版社,2004: 4-20页
[103]吴启迪,汪镭.智能微粒群算法的研究及应用[M].南京:江苏教育出版社,2005: 10-35页
[104] Lloyd, A. R. J. M., SEAKEEPING: Ship Behaviour in Rough Weather, Ellis Horwood Limited. 1989.
[105] Bhattacharyya, R., Dynamics of Marine Vehicles, John Wiley & Sons Inc., New York, NY 1978.
[106] Bingham, H., Korsmeyer, F., and Newman, J. Prediction of the Seakeeping Characteristics of Ships [C]. 20th Symposium on Naval Hydrodynamics, Santa Barbara, CA. 1994: 27-47P
[107] Wong, H. L. A Numerical Procedure for Slender Bodies at Leeway[C]. Proc., 3rd Canadian Marine Hydrodynamics and Structures Conference, Halifax/Dartmouth, Nova Scotia, Canada. 1995: 83-62 P
[108] Wu Y., Xia J., and Du S. Two Engineering Approaches to Hydroelastic Analysis of Slender Ships [C]. Proc., International Union of Theoretical and Applied Mechanics Memorial Symposium on the Dynamics of Marine Vehicles and Structures in Waves, Brunel University, Uxbridge, U.K. 1990,June 24-27: 157-165P
[109] Cohen, S. B., and Beck, R. F. Experimental and Theoretical Hydrodynamic Forces on a Mathematical Model in Confined Waters [J]. J. Ship Res. 1983, 27(2): 75-89P
[110] Nahon, M., A. Simplified Dynamics Model for Autonomous Underwater Vehicles [C]. Proc., Symposium on Autonomous Underwater Vehicle Technology, Monterey, CA, 1996, June 2-6: 373-379P
[111] Roerdink J,Meijster A.The Watershed Transform:Definitions,Algorithmsand Parallelization Strategies [J].Fundamenta Informaficae,2000,41(1-2):187-228P
[112]蔡金狮.飞行器气动参数辨识进展[J].力学进展. 1987, 17(4): 467-477页
[113]王树桂.系统辨识及航行器参数辨识进展[J].昆明工学院学报. 1995, 20(6), 8-13页
[114] Abkowitz.M.A. Measurement of Hydrodynamic Characteristics From Ship Manoeuvring Trails by System Identification [J]. Transactio-ns-Society of Naval Architects and Marine Engineers. 1980, (88): 283-318P
[115] Clarke, D, P. and Hine, G.. The Application of Manoeuvring Criteria in Hull Design Using Linear Theory [J]. Transactions of the Royal Institution of Naval Architects, 1982: 45-68P
[116] Haddara, M.R. On the use of neural network techniques for the identification of ship stability parameters at sea [C]. Proceedings of the Fourteenth International Conference on Offshore Mechanics and Arctic Engineering, vol. II, Copenhagen, Denmark, 1995: 127-135P
[117] Haddara, M. R., On the Random Decrement for Nonlinear Rolling Motion[C]. Proc., 12th International Conference on Offshore Mechanics and Arctic Engineering, Calgary, Canada, 1992: 283-288P
[118] Haddara, M.R., Wang, Y., Parametric identification of coupled sway and yaw motions [C]. Proceedings of the Fifteenth International Conference on Offshore Mechanics and Arctic Engineering, vol. I, Florence, Italy, 1996: 267-273P
[119] Haddara, M. R., and Xu, J. On the Use of Random Decrement in the Identification of Two Degrees of Freedom Systems [C]. Proc. CSME FORUM, Ryerson Polytechnic University, Toronto, 1998, May 19-22, (4): 499-507P
[120] Haddara. M. R. Jinsong Xu. On the identification of ship coupled heave-pitch motions using neural networks [J]. Ocean Engineering, 1997, 26(5): 381-400P
[121] Ayman B. Mahfouz , Mahmoud R. Haddara. Effect of the damping and excitation on the identification of the hydrodynamic parameters for an underwater robotic vehicle [J]. Ocean Engineering. 2003, (30): 1005-1025P
[122]丁文镜,罗仁凡等.受控航行体水动参数的极大似然辨识[J].清华大学学报(自然科学版),1992,2(32): 89-95页
[123]丁文镜,罗仁凡等.水下航行体水动力参数的仿真辨识[J].兵工学报. 1992, (4): 76-83页
[124]邹早建,吴秀恒.船舶操纵性非线性KT方程参数的辨识[J].武汉水运工程学院学报. 1985, (3): 11-22页
[125]蔡泽伟.船舶操纵运动方程系数的系统辨识[J].宁波大学学报. 1989, 2(1): 88-98页
[126]徐孟,陈永冰,周永余.基于高级遗传算法的船舶模型参数辨识[J].舰船电子工程. 2006,26(1): 101-106页
[127]林莉,万德钧,李滋刚.基于人工神经网络的船舶运动数学模型的辨识[J].东南大学学报(自然科学版). 2000,30(2): 71-74页
[128] LUO Wei- lina, b, ZOU Zao- jian. Identification of Response Models of Ship Maneuver ing Motion Using Suppor t Vector Machines [J]. Journal of Ship Mechanics. 2007, 11(6): 832-838P
[129]赵大明,施朝健,彭静.应用扩展卡尔曼滤波算法的船舶运动模型参数辨识[J].上海海事大学学报. 2008.29(3): 5-9页
[130]段文洋,马山.船舶航行时水动力系数求解二维半理论的稳定算法[J].船舶力学, 2004, 8(4): 27-34页
[131]陈玮琪,颜开,史淦君,王士同,刘志勇.水下航行体水动力参数智能辨识方法研究[J].船舶力学. 2007,11(1): 40-46页
[132]陈玮琪,颜开,史淦君,鲁海燕.智能算法和物体斜出水水动力参数辨识[J].船舶力学. 2008, 12(2): 204-210页
[133] Millonas, M.M.Swarm, phase transitions, and collective intelligence [J]. In C.G. Langton, Ed., Artificial Lifr III. Addison Wesley, Reading, MA.1994
[134] Eric Bonabeau, Marco Dorigo, Guy Theraulaz. Swarm Intelligence: From Nature to Artificial Systems. New York: Oxford University Press. 1999
[135] Colorni, A., Dorigo, M., and Maniezzo, V., An investigation of some Properties of an ant algorithm [C]. Proceedings of the parallel Problem Solving from Nature Conference(PPSN 92), Brussels, Beigium, Manner, R. and Manderick, B.(Eds.), Elsevier Publishing, 1992: 509-520 P
[136] Dorigo, M., Maniezzo, V., Colorni, A. Ant system: optimizaion by a colony of cooperating agents[C]. IEEE Transaction on Systems, Man and Cybernetics-Part B, 1996,26(1):1-26 P
[137]段晓东,王存睿,刘向东等.粒子群优化算法及其应用[M].辽宁大学出版社,2007:1-40页
[138]纪震,廖惠连,吴青华.粒子群优化算法及应用[M].北京:科学出版社.2008: 9-45页
[139]王凌,刘波.微粒群优化与调度算法[M].北京:清华大学出版社. 2008: 36-39页
[140] Kennedy J, Eberhart R C, Shi Y. Swarm Intelligence [M]. San Francisco, USA: Morgan Kaufmann, 2001: 287-323P
[141] Reynolds. Flocks, herds, and schools: A distributed behavioral model[J]. Computer Graphic, 1987,21(4):25-34 P
[142] Heppner, F. and U.Grenander. A stochastic nonlinear model for coordinated bird flocks [J]. In S.Krasner, Ed, The Ubiquity of Chaos. AAAS Publications, Washington, DC, 1990.
[143] Wilson, E.O Sociobiology: The new synthesis[M]. Belknap Press, Cambridge, MA.1975
[144] Boyd R, Recharson P. Culture and the evolutionary process [M]. Chicago: University of Chicago Press, 1985.
[145] Bandura, A. Social learning through imitation. In M.R. Jones (Ed.), Nebraska symposium on motivation. Lincoln: University of Nebraska Press. 1962: 211-269 P
[146] Bandura, A. Vicarious processes: A case of no-trial learning [J]. In L.Berkowitz(Ed.), Advances in Experimental social psychology,1965,1-55
[147] Bandura, A.Behavior theory and the models of man [J]. American Psychologist, 29, 1974: 859-869 P
[148] Bandura, A.Social foundations of thought and action: a social cognitive theory[J]. Englewood Cliffs, MJ:Prentice-Hall.1986
[149] Kennedy J. The particle swarm: Social adaptation of knowledge[C]. In: IEEE International Conference on Evolutionary Computation. Indianapolis, IN, USA, 1997:303-308 P
[150] Clerc M. The swarm and the queen: Towards a deterministic and adaptive particle swarm optimization[C]. Proceedings of the Congress of Evolutionary Computation, Washington, 1999:1951-1957 P
[151]王立平,曹立明.遗传算法——理论、应用与软件事先[M].西安:西安交通大学出版社, 2002: 1-50页
[152] Shi Y, Eberhart R C. Empirical study of particle swarm optimization [A].Proceeding of Congress on Evolutionary Computation[C]. : Piscataway, NJ: IEEE Service Center, 1999: 1945-1949P
[153]胡寿松.自动控制原理[M].北京:国防工业出版社. 1998, 348-366页
[154]吕振肃,侯志荣.自适应变异的粒子群优化算法[J].电子学报, 2004, 32(3): 416-420页
[155]黄芳,樊晓平.基于岛屿群体模型的并行粒子群优化算法[J].控制与决策. 2006, 21(2): 175-179页
[156] Van Den Bergh F,Engelbreehi AP. AcooPerative approach to particle swarm optimization [C]. IEEE Trans. On Evolutionary ComPutation, 2004, 8(3): 225-239 P
[157] Iwamatsu M. Locating all global minima using multi-species particle swarm optimizer: the inertia weight and the constriction factor variants[C]. In: Proc. Of 2006 IEEE Congress on Evolutionary ComPutation.Vancouver, BC, Canada, 2006: 816-822P
[158] Jing Jie, Jianchao Zeng, Chongzhao Han, Qinghua Wang. Knowledge-based cooperative particle swarm optimization [J]. Applied Mathematics and Computation. 205, (2008): 861-873P
[159] S. C. Neoh, N. Morad, C. P. Lim, Z. A.Aziz. A Cascade Genetic Algorithm and particle Swarm Approach for Solving Unit Commitment and Economic Dispatch Problems[C]. Proceedings of the Second Asia International Symposium on Mechatronics. 2006.
[160]刘利强,蚁群优化方法研究及其在潜艇导航规划中的应用[D].哈尔滨工程大学博士学位论文. 2008: 65-70页
[161]王丽芳,曾建潮.基于微粒群算法与模拟退火算法的协同进化方法[J].自动化学报, 2006, 32(4): 631-635页
[162]王凌.智能优化算法及其应用[M].北京:清华大学出版社,2001
[163]李宁,孙德宝,邹彤等.基于差分方程的PSO算法粒子运动轨迹分析[J].计算机学报, 2006,29(11): 2052-2061页
[164] A.EI-Gallad, M.EI-Hawary, A.Sallam, A.Kalas. Enhancing the Particle Swarm Optimizer Via Proper Parameters Selection[C]. Proceedings of the 2002 IEEE Canadian Conference on Electrical & Computer Engineering. 2002: 792-797P
[165] M.Jiang, Y.P. Luo, S.Y. Yang. Stochastic convergence analysis andparameter selection of the standard particle swarm optimization algorithm [J]. Information Processing Letters. 2007, 102: 8-16P
[166] Nayan R. Samal, Amit Konar, Atulya Nagar. Stability Analysis and Parameter Selection of a Particle Swarm Optimizer in a Dynamic Environment[C]. Second UKSIM European Symposium on Computer Modeling and Simulation.2008: 21-27P
[167]王俊伟,汪定伟.粒子群优化算法中惯性权重的实验与分析[J].系统工程学报, 2005, 20(2):195-199页
[168] Dai YT, Liu LQ, Wang SJ. Parameter optimization of ant colony algorithm based on particle swarm optimization[C]. Proceedings of the International Conference Information Computing and Automation. Vols 1-3, 2008: 1266-1269P
[169]李敏强,寇纪淞,林丹,李书全著.遗传算法的基本理论与应用[M].北京:科学出版社, 204: 1-15页
[170]付国江,王少梅,刘舒燕等.含边界变异的粒子群优化算法[J].武汉理工大学学报, 2005 ,27 (9): 101-103页
[171]朱丽莉,杨志鹏,袁华.粒子群优化算法分析及研究进展[J] .计算机工程与应用, 2007,43 (5): 24-27页
[172]杨维,李歧强,粒子群优化算法综述[J].中国工程科学. 2004,6(5),87-93.
[173]田雨波,朱人杰,薛权祥.粒子群优化算法中惯性权重的研究进展[J].计算机工程与应用. 2008, 44(23): 39-41页.
[174]董元,王勇,易克初.粒子群优化算法发展综述[J].商洛学院学报. 2006, 20(4): 28-33页
[175] Hosam E., Mohammed S., Khaled H. Parameter Estimation of Wiener-Hammerstein Models [J]. JSME International Journal, 2001, 44(1): 118-124P
[176] Boutayeb M, Darouach M. Recursive identification method for MISO Wiener-Hammerstein model [J]. IEEE Trans. on Automatic Control, 1995, 40 (2): 287-291P
[177] Alain Y. Kibangou, Gérard Favier. Toeplitz-Vandermonde Matrix Factorization with Application to Parameter Estimation of Wiener–Hammerstein Systems [J]. IEEE Signal Process Letters. 2007, 14(2) : 141-144P
[178] Jing Ke, Chengjin Zhang, and Yizheng Qiao. Modified Evolution StrategyBased Identification of Multi-Input Single-Output Wiener-Hammerstein Model[C]. International Conference on Natural Computation. 2007: 239-243P
[179]张顶学,廖锐全.一种基于种群速度的自适应粒子群优化算法[J].控制与决策. 2009, 24(8): 1257-1265页
[180] Zhang L P, Yu H J, Hu S X. A new approach to improve particle swarm optimization[C]. Lecture Notes in Computer Science. Chicago: Springer-Verlag, 2003:134-139P
[181] Jiang C W, Etorre B. A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimization [J]. Mathematics and Computers in Simulation, 2005, 68 (1): 57-65P
[182] Jiao B, Lian Z G, Gu X S. A dynamic inertia weight particle swarm optimization algorithm [J]. Chaos Solitons & Fractals, 2008, 37 (3): 698-705P
[183] D.H. Zhou, P.M. Frank, A real-time estimation approach to time-varying time delay and parameters of NARX processes [J]. Comput.Chem. Eng. 23 (2000): 1763-1772P
[184]王凌,李文峰,郑大钟.基于一类混合策略的模型参数估计和控制器参数整定研究[J].控制与决策, 2001,16: 530-534页
[185]王凌,李令莱,郑大钟.非线性系统参数估计的一类有效搜索策略[J].自动哈学报, 2003,29: 953-958页
[186] Jiang B, Wang B W. Parameter estimation of nonlinear system based on genetic algorithms [J]. Control Theory and Applications, 2000, 17:150-152P
[187] Ling-lai Lia, Ling Wang, Li-heng Liu. An effective hybrid PSOSA strategy for optimization and its application to parameter estimation [J]. Applied Mathematics and Computation. 179, (2006): 135-146P
[188]冯铁成.船舶摇摆与操纵[M].北京:国防工业出版社,1980:1-5页
[189]顾懋祥.船舶摇摆[M].哈尔滨:军工学院出版社,1970: 1-21页
[190]董艳秋,船舶波浪外荷和水弹性[M],天津:天津大学出版社,1991: 24-25页
[191] Lewis, F.M. The inertia of water surrounding a vibrating ship[J], Trans. SNAME, 1929, 37(2): 43-60P
[192] Korvin.-Kroukovsky, B.V. Investigation of ship motions in regular waves[J], Trans. SNAME, 1955, 63(2): 75-98P
[193] Korvin-Kroukovsky, B.V.and Jacobs, W.R. pitching and heaving otions of aship in regular waves [J], Trans.SNAME, 1957, Vol.65.
[194] Salvesen, N. Tuck,K.O.and Faltinsen, O. Ship motion and sea loads[J]. SNAME, 1970, 78(4): 25-62P
[195] Borodai,I.K.and Netsvetayev,Y.A.,Ship motions in ocean waves[J](in Russian), Sudostorenie, Leningraad, 1969, 20(5): 54-70P
[196]鲁晓光.船舶流体动力系数计算的TASAI方法与计算机实现[D],天津大学硕士学位论文, 2003: 1-3P
[197]盛振邦,刘应中.船舶原理[M].上海:上海交通大学出版社. 2008: 419-426页
[198] Nao ya Umeda, Hirotada Hashimoto. Qualitative aspects of nonlinear ship motions in following and quartering seas with high forword velocity [J]. J Mar Sci Technol(2002), 6: 111-121P
[199]李学忠,张冰,李文秀.船舶控制模型的建立及仿真[J].哈尔滨工程大学学报, 17(4), 1996: 14-17页
[200] Black, J.L., Mei,C.C.znd Bray,M.C.G. Radiation and scattering of water waves by igid bodies[J], J.F.M., 1971: 77-90 P
[201]沈继红.灰色系统理论预测方法研究及其在舰船运动预报中的应用[D].哈尔滨工程大学博士论文, 2001: 77-79页
[202] J.S.Albus. A New Approach to Manipulator Control: the Cerebelllar Model Articulation Controler(CMAC)[J], J.of Dynamic Systems, Measurement and Control,1975,Sept: 220-227 P
[203] Eldracher M, Staller A, Pompl R. Adaptive Encoding Strongly Improves Function Approximation with CMAC [J]. Neural Computation, 1997, 9 (2): 403-417 P
[204]徐丽娜.神经网络控制[M].北京:电子工业出版社.2003: 33-41页
[205]闻新,周露,李翔,张宝伟. MATLAB神经网络仿真与应用[M].北京:科学出版社.2003:165-198页
[206]周姝春.CMAC学习性能及泛化性能研究综述[J].计算机仿真.2005, 22(6):5-7页
[207]罗忠,谢永斌,朱重光.CMAC学习过程收敛性的研究[J].自动化学报.1997,23(4):455-461页
[208] S.H.Lane, D.A.Handelman, J.J.Gelfand. Theory and development of higher-order CMAC neural networks [J]. IEEE Control Systems Magazine.1992, 12(4):23-30 P
[209]田景文,高美娟.人工神经网络算法研究及应用[M].北京理工大学出版社. 2006: 132-179页
[210]邓志东,孙增沂,张再兴.一种模糊CMAC神经网络[J].自动化学报, 1995,21(3): 288-294页
[211]刘冶,王耀南.一种高阶CMAC自适应控制及其应用[J].自动化学报, 2001, 27(2): 262-266页
[212] Daijin Kim. A Design of CMAC-Based Fuzzy logic Controller with Fast Learning and Accurate Approximation [J]. Fuzzy Sets and System. 2002,125: 93-104P
[213]金鸿章,李国斌.船舶特种装置控制系统[M].北京:国防工业出版社,1995: 13-84页
[214] Ljung , L. Convergence analysis of parametric Identification methods[J] . IEEE Trans. Autom. Control , 1978 , AC -23(5): 770-783P
[215] Ronald R.Dong, Joseph Katz. On the structure of bow waves on a ship model[D]. Journal of Fluid Mechanics, 1997: 346-348P
[216]傅慧萍.潜射导弹运载器水弹道动力学系统建模及其应用研究[D].西北工业大学博士学位论文.2000: 54-66页
[217]赵希人,陈虹丽,叶葵,魏东.基于扩展卡尔曼滤波的船舶纵向运动受扰力与力矩的估计[J].中国造船. 2004, 45(3): 24-30页
[218] Clarke,D, P. and Hine, G.. The Application of Manoeuvring Criteria in Hull Design Using Linear Theory [J]. Transactions of the Royal Institution of Naval Architects, 1982: 45-68P
[219]戴运桃,赵希人,刘利强.基于改进粒子群优化算法的船舶纵向运动参数辨识[J].船舶力学,2010, Vol 14, No.1-2: 44-50页
[220]李学忠,张冰.船舶控制模型的建立及仿真[J].哈尔滨工程大学学报.1996(12):14-19页
[221]刘波.多变量系统辨识中的测试信号分析与研究[D].浙江大学博士学位论文.2008: 40-56页
[222]李言俊,张科.系统辨识理论及应用[M].北京:国防工业出版社. 2003: 5-6页
[223]王科俊,姚绪梁,金鸿章.海洋运动体控制原理[M].哈尔滨:哈尔滨工程大学出版社.哈尔滨.2005: 69-91页
[224]叶葵.船舶舵减纵摇控制方法研究[D].哈尔滨工程大学硕士学位论文[D]. 2004: 25-41页
[225]李白男.伪随机信号及相关辨识[M].北京:科学出版社. 1987: 15-28页
[226] Herbert J.A.F.Tulleken. Generalized binary noise test-signal concept for improved identificatione experiment design [J]. Automatica, 1990, 26(1): 37-49P
[227] LiuBo, ZhaoJun, QianJixin. Design and Analysis of Test Signals for System Identification[C],Proceeding of International Conference on ComPutationalSeience, 2006, Vol.3, LNCS 3993: 593-600P
[228]刘波,赵均,钱积新.多通道辨识的测试信号设计与分析[C]. Proceedings of the Sixth world Congresson Intelligent Control and Automation, 2006, Vol.3: 1858-1862P
[229]陈虹丽,基于π型舵船舶纵向多变量随机控制方法研究[D].哈尔滨工程大学博士学位论文. 2004: 15-91页.
[230]金鸿章,姚绪梁.船舶控制原理[M].哈尔滨工程大学出版社, 2001: 49-61页
[231]赵尔沅,周利清,张延平.数字信号处理使用教程[M].人民邮电出版社.1999: 94-96页
[232]刘利强,于飞,谭佳琳.一种求解约束优化问题的连续域蚁群算法[J].系统仿真学报. 2008, 20(13): 3404-3407页
[233]戴运桃,赵希人,刘利强.基于连续域蚁群算法的船舶纵向运动参数辨识[J].微计算机信息,待刊
[234]戴运桃,赵希人,刘利强.基于分阶段搜索连续域蚁群算法的船舶纵向运动参数辨识[J].船舶力学,待刊
[235] Yu Jian-xing. Modeling of multi-freedom ship motion in irregular waves with fuzzy neural networks [J]. China Ocean Engineering (S0890-5487), 2002, 17(2): 255-264P
[236]赵希人,彭秀艳,尹中凤.基于水动力系数摄动的船舶横向运动姿态与受扰Kalman滤波的统计建模[J].船舶力学,2007,11(5): 702-707页
[237]唐慧妍.船舶横向运动受扰估计、建模及LQG控制研究[D]..哈尔滨工程大学博士学位论文2005: 185-186页
[238]孙宏放.基于斜舵船舶减横摇LQG控制研究[J].系统仿真学报. 2009,21(2): 503-506页
[239]郝燕玲,徐耀群,罗跃生,沈继红.最优化方法[M].哈尔滨工程大学出版社. 2001: 95-101页
[240] Shi Y, Eberhart R, A modified particleswarm optimizer[C]. In: Proc. Of the IEEE CEC. 1998: 69-73P
[241] Beck J V, Arnold K J. Parameter Estimation in Engineering and Science [M]. New York: Wiley, 1977.
[242] Saltelli A,Ratto M, Tarantola S,et al. Sensitivity analysis practices: Strategies for model-based inference [J]. Reliability Eneineering & System Safety, 2006, 91(10-11): 1109-1125P
[243] Debabrata Sen. A Study on sensitivity of maneuverability Performance on the hydrodynamic coeffieients for submerged bodies [J]. Joumal of ShiP Reseaeh, 2000, 44(3):186-196P
[244]胡坤,徐亦凡,王树宗.基于水动力系数敏感性指数的水下航行器运动方程简化研究[J].武汉理工大学学报:交通科学与工程版, 2008, 3(2): 358-361页
[245]王燕飞,朱军,张振山.评估水动力系数对潜艇操纵性影响的一种方法[J].船舶力学, 2005,9(5):61-68页
[246] Richter, G.M., et al., Sensitivity analysis for a complex crop model applied to Durum wheat in the Mediterranean [J]. Eur. J. Agron. (2009), doi: 10.1016 j.eja. 2009.09.002
[247] Doug Perrault, Neil Bose. Sensitivity of AUV added mass coefficients to variations in hull and control plane geometry [J]. Ocean Engineering 30 (2003), 645-671P
[2]贾欣乐,张显库.船舶运动智能控制与H∞鲁棒控制[M].大连:大连海事大学出版社出版.2002: 15-33页
[3]陶尧森.船舶耐波性[M].上海:上海交通大学出版社,1985: 52-83页
[4]陈虹丽,刘贵栋,赵希人等.船舶纵向运动水动力参数模型的建立及分析[J].仪器仪表学报. 2006, 27(6): 1120-1121页
[5] Kennedy J,Eberhart R C.Particle swarm optimization proc[A].In:IEEE Service Center ed.IEEE International Conference on Neural Networks[C],Perth,Australia,995.Piscataway: IEEE Press,1995: 1942-1948P
[6] Eberhart R, Kennedy J. A New Optimizer Using Particle Swarm Theory [A]. In: Proc of the Sixth International Symposium on Micro Machine and Human Science[C]. Nagoya, Japan, 1995: 39-43P
[7] Ozcan E, Mohan C k. Analysis of a simple particle swarm optimiaion system [J]. Intelligent Engineering Systems through Aritificial Neural Networks, 1998:253-258P
[8] Ozcan E, Mohan C k. Particle swarm optimization: Surfing the waves. Proceedings of the Congress on Evolutionary Computation, Piscataway[J]: IEEE Service Center, 1999:1939-1944P
[9] Clerc M,and Kennedy J. The Particle Swarm: ExPlosion, Stability, and Convergence in a ComPlex Space [J], IEEE Transaction On Evolutionary ComPutation, 2002, 6(l):58-73P
[10] F. van den Bergh. An Analysis of Particle Swarm Optimizers: [PHD Thesis].Department of Computer Science, University of Pretoria, South Africa, 2002.
[11]高尚,汤可宗等.粒子群优化算法收敛性分析[J].科学技术与工程. 2006, 6(12): 1625-1631P
[12] Shi Y, Eberhart R C. A modified particle swarm optimizer [A], IEEE Int. Conf. on Evolutionary Computation [C], Piscataway, NJ, IEEE Service Center, 1998: 69-73P
[13] Eberhart R C, Shi Y. ComParing inertia weigthts and constriction factors in particle swarm optimization[C]. Proeeedings of the IEEE Congress on Evolutionary ComPutation (CEC2000), SanDiego, CA.2000: 84-88P
[14] Shi Y, Eberhart R C. Parameter selection in particle swarm optimization [A], Proceedings of the Seventh Annual Conference on EvolutionaryProgramming[C], Washington DC, 1998: 591-600P
[15] Shi Y, Eberhart R C. Empitical study of particle swarm optimization [A], proceeding of the Congress on Evolutionary Computation[C], Washington DC, 1999: 1945-1950P
[16] Suganthan P N. Particle swarm optimizer with neighborhood operator [A]. Proc.of the Congress on Evolutionary Computation[C], Washington DC, 1999: 1958-1962P
[17] Shi Yuhui, Eberhart R C. Fuzzy adaptive particle swarm optimization [A], Proc. IEEE. Int Conf on Evolutionary Computation [C], Seoul, Korea, 2001: 101-106P
[18]王俊伟,汪定伟.粒子群优化算法中惯性权重的实验与分析[J].系统工程学报, 2005, 20(2): 194-198P
[19] I. C. Trelea. The particle swarm optimization algorithm: Convergence analysis and parameter selection [J]. Information Processing Letters, 2003, 85 (6): 317-325P
[20] M.Jiang, Y.P. Luo, S.Y. Yang. Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm [J]. Information Processing Letters. 2007, 102: 8-16P
[21] Nayan R. Samal, Amit Konar, Atulya Nagar. Stability Analysis and Parameter Selection of a Particle Swarm Optimizer in a Dynamic Environment[C]. Second UKSIM European Symposium on Computer Modeling and Simulation.2008: 21-27P
[22] Ratnaweera A, Halgamuge S, Watson H. Self-organizing hierarehical Particle swarm optimizer with time varying accelerating coeffieients[C]. IEEE Trans. Evol. ComPut, vol.8, Jun.2004: 240-255P
[23] Monson C K, Sepp K D. The Kalman Swarm- A New Approach to Particle Motion in Swarm Optimization[C]. Proceedings of the Genetic and Evolutionary Computation Conference. Springer, 2004: 140-150P
[24] Zielinski Karin, Laur Rainer. Adaptive parameter setting for a multi-objective particle swarm optimization algorithm[C]. 2007 IEEE Congress on Evolutionary ComPutation, CEC 2007: 3019-3026P
[25]王维博,林川,郑永康.粒子群优化算法中参数的实验与分析[J].西华大学学报自然科学版2008,27(1): 76-80页
[26]潘冠宇,刘大有,窦全胜,刘晓华. PSO方法的收敛性及基于微分演化的参数确定策略[J].吉林大学学报(工学版). 2007, 37(4): 842-845P
[27] Kennedy J. Small Worlds and Mega-minds: Effects of NeighborhoodTopology on Particle Swarm Performance[C]. In: The 1999 Congress on Evolutionary Computation. Piscataway, NJ: IEEE Press, 1999: 1931-1938P
[28] Kennedy J, R Mendes. Topological Structure and Particle Swarm Performance[C]. In: The 2002 Congress on Evolutionary Computation.Piscataway, NJ: IEEE Service Center, 2002: 1671-1676P
[29] Mendes R, Kennedy J. The Full Informed Particle Swarm: simpler, maybe better[C]. IEEE TRANSACTION ON EVOLUTIONARY COMPUTATION, 2004, 8(3): 204-210P
[30] Mendes R.Population Topologies and Their Influence in Particle Swarm Performance:[PHD Thesis]. Department of Information, University of Minho, Braga, Portugal. 2004.
[31] Angeline P J. Evolutionary optimization versus particle warm optimization: Philosophy and performance difference [A], Proc of the 7th Annual Conf on Evolutionary Programming [C], Gemany: Springer, 1998: 601-610P
[32] Riget J, Vesterstr?m J S. A diversity-guided particle swarm optimization-the ARPSO, EVALife Technical Report no.2002-02P
[33] L?vbjerg M, Krink T. Extending particle swarms with self-organized criticality [A], Proceedings of the Fourth Congress on evolutionary computation [C], Honolulu, HI USA, 2002, 2: 1588-1593P
[34] Krink T, Vesterstr?m J S, Riget J. Particle swarm optimization with spatial particle extension[A], Proceedings of the Fourth Congress on Evolutionary Computation[C], Honolulu, HI USA, 2002, 2:1474-1479P
[35] Angeline P J. Using Selection to Improve Particle Swarm Optimization [R], IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, 1998.
[36] Higashi N, Iba H. Particle Swarm Optimization with Gaussian Mutation[C]. In: The 2003 Congress on Evolutionary Computation. Piscataway, NJ: IEEE Press, 2003: 72-79P
[37] NING LI, YUAN-QING QIN, DE-BAO SUN, TONG ZOU. Particle Swarm Optimization with Mutation Operator[C]. In: The Third International Conference on Machine Learning and Cybernetics. Piscataway, NJ: IEEE Press, 2004: 2251-2256P
[38]吕振肃,侯志荣.自适应变异的粒子群优化算法[J].电子学报,2004,32(3):416-420页.
[39] Baskar S, Suganthan P. N. A Novel Concurrent Particle Swarm Optimization[C]. In: The 2004 Congress on Evolutionary Computation. Piscataway, NJ:IEEE Press, 2004:792-796P
[40] Shi Y, Krohling R. A. Co-evolutionary Particle Swarm Optimization to Solve Min-max Problems[C]. In: The 2002 Congress on Evolutionary Computation. Piscataway, NJ:IEEE Press,2002:1682-1687P
[41] Vesterstr?m J. S, Riget J and Krink T. Division of Labor in Particle Swarm Optimisation[C]. In: The 4thCongress on Evolutionary Computation. Piscataway, NJ: IEEE Press, 2002: 1570-1575P
[42] Janson S, Middendorf M. A Hierarchical Particle Swarm Optimizer[C]. In: The 2003 Congress on Evolutionary Computation. Piscataway, NJ: IEEE Press, 2003: 770-776P
[43]陈民铀,张聪誉,罗辞勇.自适应进化多目标粒子群优化算法[J].控制与决策, 2009,24(12): 1851-1855页
[44] L?vbjerg M, Rasmussen T. K, and Krink T. Hybrid Particle Swarm Optimizer with Breeding and Subpopulations[C]. In: Third Genetic and Evolutionary Computation Conference.Piscataway, NJ:IEEE Service Center, 2001: 469-476P
[45]胡广浩,毛志忠,何大阔.基于遗传和粒子群结合的文化算法[J].东北大学学报, 2009, 30(11): 1542-1545页
[46] Yang S,Wang, M and Jiao L.A Quantum Particle Swarm Optimization[C]. In: The 2004 Congress on Evolutionary Computation. Piscataway, NJ:IEEE Press, 2004:320-324P
[47] Zhisheng Zhang, Quantum-behaved particle swarm optimization algorithm for economic load dispatch of power system [J]. Expert Systems with Applications, 2010, 37(2): 1800-1803P
[48] Jun Sun,Bin Feng and Wenbo Xu.Particle Swarm Optimiztion with Particles Having Quantum Behavior.In:The 2004 Congress on Evolutionary Computation.Piscataway,NJ:IEEE Press, 2004: 325-331P
[49] Wang Xihuai, Li Junjun. Hybrid Particle Swarm Optimization with Simulated Annealing. In:The 3rd International Conference on Machine Learning and Cybernetics. Piscataway, NJ: IEEE Press, 2002: 2402-2405P
[50] Xie X, Zhang W and Yang Z. A Dissipative Particle Swarm Optimization [C]. In: The 2002 Congress on Evolutionary Computation. Piscataway, NJ: IEEE Press, 2002: 1456-1461P
[51] Xie X, Zhang W and Yang Z. Adaptive Particle Swarm Optimization on Individual Level [J]. In: International Conference on Signal Processing. Piscataway, NJ: IEEE Service Center, 1998: 1215-1218P
[52] Hui Liu, Zixing Cai, Yong Wang. Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization [J]. Applied Soft Computing. 10, (2010): 629-640P
[53] Nakano S, IshigameA, Yasuda K. Particle swam optimization based on the concept of Tabu search [C]. 2007 IEEE Congress on Evolutionary Computation, 2007: 3258-63P
[54] Bo Liu, Ling Wang, Yi-Hui Jin, Fang Tang, De-Xian Huang. Improved particle swarm optimization combined with chaos [J]. Chaos, Solitons and Fractals. 25, (2005): 1261-1271P
[55] P.S. Shelokar, Patrick Siarry, V.K. Jayaraman, B.D. Kulkarni. Particleswarm and ant colony algorithms hybridized for improved continuous optimization [J]. Applied Mathematics and Computation, 188, (2007): 129-142P
[56]吕强,刘士荣,邱雪娜.基于信息素机制的粒子群优化算法的设计与实现[J].自动化学报, 2009, 35 (11): 1410-1419页
[57]高鹰,谢胜利.免疫粒子群优化算法[J].计算机工程与应用, 2004, 40(6): 4-6页
[58]邹彤,李宁,孙德宝,岑翼刚.带阴性选择的粒子群优化算法[J].华中科技大学学报(自然科学版), 2006, 34(2):87-90页
[59] R.Brits, A. P. Engelbrecht and F.van den Bergh. A Niching Particle Swarm Optimizer[C]. In: The 4th Asia-Pacific Conference on Simulated Evolution and Learning. Piscataway, NJ: IEEE Press, 2002: 692-696P
[60]周殊,潘炜,罗斌.一种基于粒子群优化方法的改进量子遗传算法及应用[J].电子学报, 2006, 34(5): 897-901页
[61] ShengliSong, LiKong, Yong Gan, Rijian Su.Hybrid particle swarm cooperative optimization algorithm and its application to MBC in alumina production [J]. Progress in Natural Seience, Vol.18(11), 2008: 1423-1428P
[62] Dong qing, Wanga, Feng Ding. Extended stochastic gradient identification algorithms for Hammerstein- Wiener ARMAX systems [J]. Computers and Mathematics with Applications. 2008, 56: 3157-3164P
[63]徐小平,钱富才,王峰.一种辨识Wiener-Hammerstein模型的新方法[J].控制与决策. 2008, 23(8): 929-934页
[64]向微,陈宗海.基于模型描述的非线性系统辨识新方法[J].控制理论与应用.2007,24(1): 143-147页
[65] Hou Zhi-Xiang. Wiener model identification based on adaptive particle swarm optimization[C]. Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC.2008,vol(2):1041-1045P
[66] Wang Youbo, Wei Benzheng. A new particle swarm optimization based auto-tuning of PID controller [J]. International Journal of Control, Automation and Systems, 2009, 7(2): 273-280P
[67] Jalilvand, A., Kimiyaghalam, A., Ashouri, A.,Mahdavi, M. Advanced particle swarm optimization-based PID controller parameters tuning[C]. IEEE INMIC 2008. 12th IEEE International Multitopic Conference, 2008: 429-435P
[68]胡海兵,胡庆波,吕征宇.基于粒子群优化的PID伺服控制器设计[J].浙江大学学报(工学版), 2006, 40(12): 2144-2148页
[69] WUH, SunFC, Sun Z Q et al. Optimal Trajectory Planmng of a Flexible Dualarm Space Robot with Vibration Reduction [J]. Journal of Intelligent & RobotieSystems, 2004, 40(2): 147-163P
[70] Qing Z , Limin Q, Yingchun L et al. An improved particle swam optimization algorithm for vehicle routing problem with time windows[C]. 2006 IEEE Congress on Evolutionary Computation,CEC 2006, 2006: 1386-1390P
[71]刘海江,黄炜.基于粒子群优化算法的数控加工切削参数优化[J].同济大学学报(自然科学版), 36(6), 2008, 803-506页
[72] Yang L, Hu W W, Yang S Y et al. Application of Particle Swam Optimization in Multi-sensor Multi-target Traeking [C]. 1st International Symposium on Systems and Control in Aerospace and Astronautics, 2006: 715-719P
[73]胡炜薇,杨雷,杨萃元,廖艳苹.粒子群优化算法在多传感器多目标跟踪的应用[J].哈尔滨工程大学学报,28(1), 2007: 102-107页
[74]张文静,赵先章,台宪青.基于粒子群优化算法的火炮伺服系统摩擦参数辨识[J].清华大学学报(自然科学版), 2007, 47(s2): 1717-1720页
[75] Liu Y, Zhang H, et al, Particle Swarm Optimization for Fault State Power Supply Reliability Enhancement [C]. In: Proc of the IEEE International Conference on Intelligent Systems Application to Power Systems, Budapest, Hungary, 2001: 172-176[J]
[76] Yoshida H. AParticle swam optimization for reactive power and voltage control considering voltage security assessment [J]. Transactions of the Institute of Electrical Engineers of Japan, 1999, 119-B(12): 1462-1469P
[77]张凌杰,张国辉.电力系统经济负荷分配的混合粒子群优化算法.计算机工程与应用. 2009, 45(33): 202-205页
[78] Naka S. A hybrid particle swarm optimization for distribution state estimation [J]. IEEE Transactions on power Systems 2003, 18(1), 60-68P
[79]张智晟,董存,吴新振.基于量子粒子群优化算法的水电系统经济运行[J]. 2009, 33(18): 68-72页
[80] Gaing Z L.Particle Swarm Optimization to Solving the Economic Dispatch Considering the Generator Constrainis [J]. IEEE Transactions on Power Systems, 2003, 18(3): 1187-1195P
[81] Yoshida H,Kawata K,Fukuyama Y.et al.A Particle Swarm Optimization for Reactive Power and Voltage Control Considering Voltage Stability[C]. In:Proc of the International Conference on Intelligent System Application to Power Systems.Rio de Janeiro, Brazil, 1999: 1l7-121P
[82] Sensarma P S, Rahmani M, Carvalho A. A comprehensive method for optimal expansion planning using particle swarm optimization [C]. IEEE Power Engineering Society Winter Meeting, 2002, 2: 1317-1322P
[83]肖军,刘天琪,苏鹏.基于双种群粒子群优化算法的分时段电力系统无功优化[J].电网技术. 2009, 33(8): 72-77页
[84]陈华东,王树宗,王航宇.基于混合粒子群优化算法的多平台多武器火力分配研究[J].系统工程与电子技术, 2008, 30(5): 880-883页
[85] Xiangping Zeng,Yunlong Zhu, Lin Nan1, et al. Solving weapon-target assignment problem using discrete particle swarm optimization[C]. Sixth World Congress on Intelligent Control and Automation. 2006, 21-24P
[86]单敏瑜,刘以安,倪天权. QPSO在无人机侦察航路规划中的应用研究[J].计算机工程与设计. 2009,30(20): 4690-4692,4773页
[87] Ding Zhu, Ma Da-wei, Tang Ming-duan, Zhang Xue-feng. TSAPSO: a hybrid search algorithm of tabu search and annealing particle swarm optimization for weapon-target assignment [J]. Journal of System Simulation. 2006, 18(9): 2480-2483P
[88]孔国杰,张培林,徐龙堂,吴烽.一种新的火炮初速下降量预测模型.弹道学报. 2009, 21(3): 65-68页
[89]王国栋,李明,陈希成,范彦铭.基于改进的粒子群优化算法的无人作战飞机航路规划[J].航空计算技术. 2007,37(4): 9-13页
[90] Jun Sun, C. H. Lai, Wenbo Xu. Empirical study on stock market through ARCH model based on particle swarm optimization. DCABE 2006 Proceedings. 2006: 827-831P
[91]朱嘉瑜,叶海燕,高鹰.基于隐马尔可夫模型的股票价格预测组合模型[J].计算机工程与设计. 2009,30 (21): 4945-4938页
[92]周辉仁,郑丕谔,王嵩,刘春霞.基于粒子群优化算法的LS-SVM财务预警[J].计算机工程. 2009, 35(9): 280-282页
[93] Briza Antonio C, Naval Jr, Prospero C. Design of stock trading system for historical market data using multiobjective particle swarm optimization of technical indicators [CA]. 10th Annual Genetic and Evolutionary Computation Conference, 2008: 1871-1878P
[94]岑翼刚,秦元庆,孙德宝.粒子群优化算法在小波神经网络中的应用[J].系统仿真学报. 2004, 16(12): 2783-2785页
[95] EI-Telbany M. A., Konsowa H.G.,EI-Adawy M., Studying the predictability of neural network trained by particle swam optimization [J]. Journal of Engineering and Applied Scienee, 2006, 53(3): 377-390P
[96] Su Rijian, Kong Li, Song Shengli et al. A new ridgelet neural network training algorithm based on improved particle swarm optimization[C]. Proeeedings-Third International Conference on Natural Computation, ICNC 2007, 2007, vol.3: 411-415P
[97] CarvalhoMareio, LudermirTeresaB, Particle swam optimization of neural network architectures and weights [C]. Proeeedings-7th International Conference on Hybrid Intelligent Systems, HIS2007, 2007: 336-339P
[98] Ismail A, Engelbreeht A. Training Product units in feedforward neural networks using particle swarm optimization [C]. Proeeeding of the International Conference on Artificial intelligence. Durban, South Africa, 1999: 36-40P
[99]都延丽,吴庆宪,姜长生,周丽.改进协同微粒群优化的模糊神经网络控制系统设计[J].控制与决策. 2008, 23(12): 1328-1337页
[100] Eberhart R, Hu X H. Human tremor analysis using particle swam optimization[C]. Proeeedings of the 1999 Congress on Evolutionary Computation. Piscataway, NJ, USA: IEEE, 1999: 1927-1930P
[101]康琦.微粒群优化算法的研究与应用[D].上海:同济大学, 2005: 4-10页
[102]吴启迪,汪镭.智能蚁群算法及应用[M].上海:上海科技教育出版社,2004: 4-20页
[103]吴启迪,汪镭.智能微粒群算法的研究及应用[M].南京:江苏教育出版社,2005: 10-35页
[104] Lloyd, A. R. J. M., SEAKEEPING: Ship Behaviour in Rough Weather, Ellis Horwood Limited. 1989.
[105] Bhattacharyya, R., Dynamics of Marine Vehicles, John Wiley & Sons Inc., New York, NY 1978.
[106] Bingham, H., Korsmeyer, F., and Newman, J. Prediction of the Seakeeping Characteristics of Ships [C]. 20th Symposium on Naval Hydrodynamics, Santa Barbara, CA. 1994: 27-47P
[107] Wong, H. L. A Numerical Procedure for Slender Bodies at Leeway[C]. Proc., 3rd Canadian Marine Hydrodynamics and Structures Conference, Halifax/Dartmouth, Nova Scotia, Canada. 1995: 83-62 P
[108] Wu Y., Xia J., and Du S. Two Engineering Approaches to Hydroelastic Analysis of Slender Ships [C]. Proc., International Union of Theoretical and Applied Mechanics Memorial Symposium on the Dynamics of Marine Vehicles and Structures in Waves, Brunel University, Uxbridge, U.K. 1990,June 24-27: 157-165P
[109] Cohen, S. B., and Beck, R. F. Experimental and Theoretical Hydrodynamic Forces on a Mathematical Model in Confined Waters [J]. J. Ship Res. 1983, 27(2): 75-89P
[110] Nahon, M., A. Simplified Dynamics Model for Autonomous Underwater Vehicles [C]. Proc., Symposium on Autonomous Underwater Vehicle Technology, Monterey, CA, 1996, June 2-6: 373-379P
[111] Roerdink J,Meijster A.The Watershed Transform:Definitions,Algorithmsand Parallelization Strategies [J].Fundamenta Informaficae,2000,41(1-2):187-228P
[112]蔡金狮.飞行器气动参数辨识进展[J].力学进展. 1987, 17(4): 467-477页
[113]王树桂.系统辨识及航行器参数辨识进展[J].昆明工学院学报. 1995, 20(6), 8-13页
[114] Abkowitz.M.A. Measurement of Hydrodynamic Characteristics From Ship Manoeuvring Trails by System Identification [J]. Transactio-ns-Society of Naval Architects and Marine Engineers. 1980, (88): 283-318P
[115] Clarke, D, P. and Hine, G.. The Application of Manoeuvring Criteria in Hull Design Using Linear Theory [J]. Transactions of the Royal Institution of Naval Architects, 1982: 45-68P
[116] Haddara, M.R. On the use of neural network techniques for the identification of ship stability parameters at sea [C]. Proceedings of the Fourteenth International Conference on Offshore Mechanics and Arctic Engineering, vol. II, Copenhagen, Denmark, 1995: 127-135P
[117] Haddara, M. R., On the Random Decrement for Nonlinear Rolling Motion[C]. Proc., 12th International Conference on Offshore Mechanics and Arctic Engineering, Calgary, Canada, 1992: 283-288P
[118] Haddara, M.R., Wang, Y., Parametric identification of coupled sway and yaw motions [C]. Proceedings of the Fifteenth International Conference on Offshore Mechanics and Arctic Engineering, vol. I, Florence, Italy, 1996: 267-273P
[119] Haddara, M. R., and Xu, J. On the Use of Random Decrement in the Identification of Two Degrees of Freedom Systems [C]. Proc. CSME FORUM, Ryerson Polytechnic University, Toronto, 1998, May 19-22, (4): 499-507P
[120] Haddara. M. R. Jinsong Xu. On the identification of ship coupled heave-pitch motions using neural networks [J]. Ocean Engineering, 1997, 26(5): 381-400P
[121] Ayman B. Mahfouz , Mahmoud R. Haddara. Effect of the damping and excitation on the identification of the hydrodynamic parameters for an underwater robotic vehicle [J]. Ocean Engineering. 2003, (30): 1005-1025P
[122]丁文镜,罗仁凡等.受控航行体水动参数的极大似然辨识[J].清华大学学报(自然科学版),1992,2(32): 89-95页
[123]丁文镜,罗仁凡等.水下航行体水动力参数的仿真辨识[J].兵工学报. 1992, (4): 76-83页
[124]邹早建,吴秀恒.船舶操纵性非线性KT方程参数的辨识[J].武汉水运工程学院学报. 1985, (3): 11-22页
[125]蔡泽伟.船舶操纵运动方程系数的系统辨识[J].宁波大学学报. 1989, 2(1): 88-98页
[126]徐孟,陈永冰,周永余.基于高级遗传算法的船舶模型参数辨识[J].舰船电子工程. 2006,26(1): 101-106页
[127]林莉,万德钧,李滋刚.基于人工神经网络的船舶运动数学模型的辨识[J].东南大学学报(自然科学版). 2000,30(2): 71-74页
[128] LUO Wei- lina, b, ZOU Zao- jian. Identification of Response Models of Ship Maneuver ing Motion Using Suppor t Vector Machines [J]. Journal of Ship Mechanics. 2007, 11(6): 832-838P
[129]赵大明,施朝健,彭静.应用扩展卡尔曼滤波算法的船舶运动模型参数辨识[J].上海海事大学学报. 2008.29(3): 5-9页
[130]段文洋,马山.船舶航行时水动力系数求解二维半理论的稳定算法[J].船舶力学, 2004, 8(4): 27-34页
[131]陈玮琪,颜开,史淦君,王士同,刘志勇.水下航行体水动力参数智能辨识方法研究[J].船舶力学. 2007,11(1): 40-46页
[132]陈玮琪,颜开,史淦君,鲁海燕.智能算法和物体斜出水水动力参数辨识[J].船舶力学. 2008, 12(2): 204-210页
[133] Millonas, M.M.Swarm, phase transitions, and collective intelligence [J]. In C.G. Langton, Ed., Artificial Lifr III. Addison Wesley, Reading, MA.1994
[134] Eric Bonabeau, Marco Dorigo, Guy Theraulaz. Swarm Intelligence: From Nature to Artificial Systems. New York: Oxford University Press. 1999
[135] Colorni, A., Dorigo, M., and Maniezzo, V., An investigation of some Properties of an ant algorithm [C]. Proceedings of the parallel Problem Solving from Nature Conference(PPSN 92), Brussels, Beigium, Manner, R. and Manderick, B.(Eds.), Elsevier Publishing, 1992: 509-520 P
[136] Dorigo, M., Maniezzo, V., Colorni, A. Ant system: optimizaion by a colony of cooperating agents[C]. IEEE Transaction on Systems, Man and Cybernetics-Part B, 1996,26(1):1-26 P
[137]段晓东,王存睿,刘向东等.粒子群优化算法及其应用[M].辽宁大学出版社,2007:1-40页
[138]纪震,廖惠连,吴青华.粒子群优化算法及应用[M].北京:科学出版社.2008: 9-45页
[139]王凌,刘波.微粒群优化与调度算法[M].北京:清华大学出版社. 2008: 36-39页
[140] Kennedy J, Eberhart R C, Shi Y. Swarm Intelligence [M]. San Francisco, USA: Morgan Kaufmann, 2001: 287-323P
[141] Reynolds. Flocks, herds, and schools: A distributed behavioral model[J]. Computer Graphic, 1987,21(4):25-34 P
[142] Heppner, F. and U.Grenander. A stochastic nonlinear model for coordinated bird flocks [J]. In S.Krasner, Ed, The Ubiquity of Chaos. AAAS Publications, Washington, DC, 1990.
[143] Wilson, E.O Sociobiology: The new synthesis[M]. Belknap Press, Cambridge, MA.1975
[144] Boyd R, Recharson P. Culture and the evolutionary process [M]. Chicago: University of Chicago Press, 1985.
[145] Bandura, A. Social learning through imitation. In M.R. Jones (Ed.), Nebraska symposium on motivation. Lincoln: University of Nebraska Press. 1962: 211-269 P
[146] Bandura, A. Vicarious processes: A case of no-trial learning [J]. In L.Berkowitz(Ed.), Advances in Experimental social psychology,1965,1-55
[147] Bandura, A.Behavior theory and the models of man [J]. American Psychologist, 29, 1974: 859-869 P
[148] Bandura, A.Social foundations of thought and action: a social cognitive theory[J]. Englewood Cliffs, MJ:Prentice-Hall.1986
[149] Kennedy J. The particle swarm: Social adaptation of knowledge[C]. In: IEEE International Conference on Evolutionary Computation. Indianapolis, IN, USA, 1997:303-308 P
[150] Clerc M. The swarm and the queen: Towards a deterministic and adaptive particle swarm optimization[C]. Proceedings of the Congress of Evolutionary Computation, Washington, 1999:1951-1957 P
[151]王立平,曹立明.遗传算法——理论、应用与软件事先[M].西安:西安交通大学出版社, 2002: 1-50页
[152] Shi Y, Eberhart R C. Empirical study of particle swarm optimization [A].Proceeding of Congress on Evolutionary Computation[C]. : Piscataway, NJ: IEEE Service Center, 1999: 1945-1949P
[153]胡寿松.自动控制原理[M].北京:国防工业出版社. 1998, 348-366页
[154]吕振肃,侯志荣.自适应变异的粒子群优化算法[J].电子学报, 2004, 32(3): 416-420页
[155]黄芳,樊晓平.基于岛屿群体模型的并行粒子群优化算法[J].控制与决策. 2006, 21(2): 175-179页
[156] Van Den Bergh F,Engelbreehi AP. AcooPerative approach to particle swarm optimization [C]. IEEE Trans. On Evolutionary ComPutation, 2004, 8(3): 225-239 P
[157] Iwamatsu M. Locating all global minima using multi-species particle swarm optimizer: the inertia weight and the constriction factor variants[C]. In: Proc. Of 2006 IEEE Congress on Evolutionary ComPutation.Vancouver, BC, Canada, 2006: 816-822P
[158] Jing Jie, Jianchao Zeng, Chongzhao Han, Qinghua Wang. Knowledge-based cooperative particle swarm optimization [J]. Applied Mathematics and Computation. 205, (2008): 861-873P
[159] S. C. Neoh, N. Morad, C. P. Lim, Z. A.Aziz. A Cascade Genetic Algorithm and particle Swarm Approach for Solving Unit Commitment and Economic Dispatch Problems[C]. Proceedings of the Second Asia International Symposium on Mechatronics. 2006.
[160]刘利强,蚁群优化方法研究及其在潜艇导航规划中的应用[D].哈尔滨工程大学博士学位论文. 2008: 65-70页
[161]王丽芳,曾建潮.基于微粒群算法与模拟退火算法的协同进化方法[J].自动化学报, 2006, 32(4): 631-635页
[162]王凌.智能优化算法及其应用[M].北京:清华大学出版社,2001
[163]李宁,孙德宝,邹彤等.基于差分方程的PSO算法粒子运动轨迹分析[J].计算机学报, 2006,29(11): 2052-2061页
[164] A.EI-Gallad, M.EI-Hawary, A.Sallam, A.Kalas. Enhancing the Particle Swarm Optimizer Via Proper Parameters Selection[C]. Proceedings of the 2002 IEEE Canadian Conference on Electrical & Computer Engineering. 2002: 792-797P
[165] M.Jiang, Y.P. Luo, S.Y. Yang. Stochastic convergence analysis andparameter selection of the standard particle swarm optimization algorithm [J]. Information Processing Letters. 2007, 102: 8-16P
[166] Nayan R. Samal, Amit Konar, Atulya Nagar. Stability Analysis and Parameter Selection of a Particle Swarm Optimizer in a Dynamic Environment[C]. Second UKSIM European Symposium on Computer Modeling and Simulation.2008: 21-27P
[167]王俊伟,汪定伟.粒子群优化算法中惯性权重的实验与分析[J].系统工程学报, 2005, 20(2):195-199页
[168] Dai YT, Liu LQ, Wang SJ. Parameter optimization of ant colony algorithm based on particle swarm optimization[C]. Proceedings of the International Conference Information Computing and Automation. Vols 1-3, 2008: 1266-1269P
[169]李敏强,寇纪淞,林丹,李书全著.遗传算法的基本理论与应用[M].北京:科学出版社, 204: 1-15页
[170]付国江,王少梅,刘舒燕等.含边界变异的粒子群优化算法[J].武汉理工大学学报, 2005 ,27 (9): 101-103页
[171]朱丽莉,杨志鹏,袁华.粒子群优化算法分析及研究进展[J] .计算机工程与应用, 2007,43 (5): 24-27页
[172]杨维,李歧强,粒子群优化算法综述[J].中国工程科学. 2004,6(5),87-93.
[173]田雨波,朱人杰,薛权祥.粒子群优化算法中惯性权重的研究进展[J].计算机工程与应用. 2008, 44(23): 39-41页.
[174]董元,王勇,易克初.粒子群优化算法发展综述[J].商洛学院学报. 2006, 20(4): 28-33页
[175] Hosam E., Mohammed S., Khaled H. Parameter Estimation of Wiener-Hammerstein Models [J]. JSME International Journal, 2001, 44(1): 118-124P
[176] Boutayeb M, Darouach M. Recursive identification method for MISO Wiener-Hammerstein model [J]. IEEE Trans. on Automatic Control, 1995, 40 (2): 287-291P
[177] Alain Y. Kibangou, Gérard Favier. Toeplitz-Vandermonde Matrix Factorization with Application to Parameter Estimation of Wiener–Hammerstein Systems [J]. IEEE Signal Process Letters. 2007, 14(2) : 141-144P
[178] Jing Ke, Chengjin Zhang, and Yizheng Qiao. Modified Evolution StrategyBased Identification of Multi-Input Single-Output Wiener-Hammerstein Model[C]. International Conference on Natural Computation. 2007: 239-243P
[179]张顶学,廖锐全.一种基于种群速度的自适应粒子群优化算法[J].控制与决策. 2009, 24(8): 1257-1265页
[180] Zhang L P, Yu H J, Hu S X. A new approach to improve particle swarm optimization[C]. Lecture Notes in Computer Science. Chicago: Springer-Verlag, 2003:134-139P
[181] Jiang C W, Etorre B. A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimization [J]. Mathematics and Computers in Simulation, 2005, 68 (1): 57-65P
[182] Jiao B, Lian Z G, Gu X S. A dynamic inertia weight particle swarm optimization algorithm [J]. Chaos Solitons & Fractals, 2008, 37 (3): 698-705P
[183] D.H. Zhou, P.M. Frank, A real-time estimation approach to time-varying time delay and parameters of NARX processes [J]. Comput.Chem. Eng. 23 (2000): 1763-1772P
[184]王凌,李文峰,郑大钟.基于一类混合策略的模型参数估计和控制器参数整定研究[J].控制与决策, 2001,16: 530-534页
[185]王凌,李令莱,郑大钟.非线性系统参数估计的一类有效搜索策略[J].自动哈学报, 2003,29: 953-958页
[186] Jiang B, Wang B W. Parameter estimation of nonlinear system based on genetic algorithms [J]. Control Theory and Applications, 2000, 17:150-152P
[187] Ling-lai Lia, Ling Wang, Li-heng Liu. An effective hybrid PSOSA strategy for optimization and its application to parameter estimation [J]. Applied Mathematics and Computation. 179, (2006): 135-146P
[188]冯铁成.船舶摇摆与操纵[M].北京:国防工业出版社,1980:1-5页
[189]顾懋祥.船舶摇摆[M].哈尔滨:军工学院出版社,1970: 1-21页
[190]董艳秋,船舶波浪外荷和水弹性[M],天津:天津大学出版社,1991: 24-25页
[191] Lewis, F.M. The inertia of water surrounding a vibrating ship[J], Trans. SNAME, 1929, 37(2): 43-60P
[192] Korvin.-Kroukovsky, B.V. Investigation of ship motions in regular waves[J], Trans. SNAME, 1955, 63(2): 75-98P
[193] Korvin-Kroukovsky, B.V.and Jacobs, W.R. pitching and heaving otions of aship in regular waves [J], Trans.SNAME, 1957, Vol.65.
[194] Salvesen, N. Tuck,K.O.and Faltinsen, O. Ship motion and sea loads[J]. SNAME, 1970, 78(4): 25-62P
[195] Borodai,I.K.and Netsvetayev,Y.A.,Ship motions in ocean waves[J](in Russian), Sudostorenie, Leningraad, 1969, 20(5): 54-70P
[196]鲁晓光.船舶流体动力系数计算的TASAI方法与计算机实现[D],天津大学硕士学位论文, 2003: 1-3P
[197]盛振邦,刘应中.船舶原理[M].上海:上海交通大学出版社. 2008: 419-426页
[198] Nao ya Umeda, Hirotada Hashimoto. Qualitative aspects of nonlinear ship motions in following and quartering seas with high forword velocity [J]. J Mar Sci Technol(2002), 6: 111-121P
[199]李学忠,张冰,李文秀.船舶控制模型的建立及仿真[J].哈尔滨工程大学学报, 17(4), 1996: 14-17页
[200] Black, J.L., Mei,C.C.znd Bray,M.C.G. Radiation and scattering of water waves by igid bodies[J], J.F.M., 1971: 77-90 P
[201]沈继红.灰色系统理论预测方法研究及其在舰船运动预报中的应用[D].哈尔滨工程大学博士论文, 2001: 77-79页
[202] J.S.Albus. A New Approach to Manipulator Control: the Cerebelllar Model Articulation Controler(CMAC)[J], J.of Dynamic Systems, Measurement and Control,1975,Sept: 220-227 P
[203] Eldracher M, Staller A, Pompl R. Adaptive Encoding Strongly Improves Function Approximation with CMAC [J]. Neural Computation, 1997, 9 (2): 403-417 P
[204]徐丽娜.神经网络控制[M].北京:电子工业出版社.2003: 33-41页
[205]闻新,周露,李翔,张宝伟. MATLAB神经网络仿真与应用[M].北京:科学出版社.2003:165-198页
[206]周姝春.CMAC学习性能及泛化性能研究综述[J].计算机仿真.2005, 22(6):5-7页
[207]罗忠,谢永斌,朱重光.CMAC学习过程收敛性的研究[J].自动化学报.1997,23(4):455-461页
[208] S.H.Lane, D.A.Handelman, J.J.Gelfand. Theory and development of higher-order CMAC neural networks [J]. IEEE Control Systems Magazine.1992, 12(4):23-30 P
[209]田景文,高美娟.人工神经网络算法研究及应用[M].北京理工大学出版社. 2006: 132-179页
[210]邓志东,孙增沂,张再兴.一种模糊CMAC神经网络[J].自动化学报, 1995,21(3): 288-294页
[211]刘冶,王耀南.一种高阶CMAC自适应控制及其应用[J].自动化学报, 2001, 27(2): 262-266页
[212] Daijin Kim. A Design of CMAC-Based Fuzzy logic Controller with Fast Learning and Accurate Approximation [J]. Fuzzy Sets and System. 2002,125: 93-104P
[213]金鸿章,李国斌.船舶特种装置控制系统[M].北京:国防工业出版社,1995: 13-84页
[214] Ljung , L. Convergence analysis of parametric Identification methods[J] . IEEE Trans. Autom. Control , 1978 , AC -23(5): 770-783P
[215] Ronald R.Dong, Joseph Katz. On the structure of bow waves on a ship model[D]. Journal of Fluid Mechanics, 1997: 346-348P
[216]傅慧萍.潜射导弹运载器水弹道动力学系统建模及其应用研究[D].西北工业大学博士学位论文.2000: 54-66页
[217]赵希人,陈虹丽,叶葵,魏东.基于扩展卡尔曼滤波的船舶纵向运动受扰力与力矩的估计[J].中国造船. 2004, 45(3): 24-30页
[218] Clarke,D, P. and Hine, G.. The Application of Manoeuvring Criteria in Hull Design Using Linear Theory [J]. Transactions of the Royal Institution of Naval Architects, 1982: 45-68P
[219]戴运桃,赵希人,刘利强.基于改进粒子群优化算法的船舶纵向运动参数辨识[J].船舶力学,2010, Vol 14, No.1-2: 44-50页
[220]李学忠,张冰.船舶控制模型的建立及仿真[J].哈尔滨工程大学学报.1996(12):14-19页
[221]刘波.多变量系统辨识中的测试信号分析与研究[D].浙江大学博士学位论文.2008: 40-56页
[222]李言俊,张科.系统辨识理论及应用[M].北京:国防工业出版社. 2003: 5-6页
[223]王科俊,姚绪梁,金鸿章.海洋运动体控制原理[M].哈尔滨:哈尔滨工程大学出版社.哈尔滨.2005: 69-91页
[224]叶葵.船舶舵减纵摇控制方法研究[D].哈尔滨工程大学硕士学位论文[D]. 2004: 25-41页
[225]李白男.伪随机信号及相关辨识[M].北京:科学出版社. 1987: 15-28页
[226] Herbert J.A.F.Tulleken. Generalized binary noise test-signal concept for improved identificatione experiment design [J]. Automatica, 1990, 26(1): 37-49P
[227] LiuBo, ZhaoJun, QianJixin. Design and Analysis of Test Signals for System Identification[C],Proceeding of International Conference on ComPutationalSeience, 2006, Vol.3, LNCS 3993: 593-600P
[228]刘波,赵均,钱积新.多通道辨识的测试信号设计与分析[C]. Proceedings of the Sixth world Congresson Intelligent Control and Automation, 2006, Vol.3: 1858-1862P
[229]陈虹丽,基于π型舵船舶纵向多变量随机控制方法研究[D].哈尔滨工程大学博士学位论文. 2004: 15-91页.
[230]金鸿章,姚绪梁.船舶控制原理[M].哈尔滨工程大学出版社, 2001: 49-61页
[231]赵尔沅,周利清,张延平.数字信号处理使用教程[M].人民邮电出版社.1999: 94-96页
[232]刘利强,于飞,谭佳琳.一种求解约束优化问题的连续域蚁群算法[J].系统仿真学报. 2008, 20(13): 3404-3407页
[233]戴运桃,赵希人,刘利强.基于连续域蚁群算法的船舶纵向运动参数辨识[J].微计算机信息,待刊
[234]戴运桃,赵希人,刘利强.基于分阶段搜索连续域蚁群算法的船舶纵向运动参数辨识[J].船舶力学,待刊
[235] Yu Jian-xing. Modeling of multi-freedom ship motion in irregular waves with fuzzy neural networks [J]. China Ocean Engineering (S0890-5487), 2002, 17(2): 255-264P
[236]赵希人,彭秀艳,尹中凤.基于水动力系数摄动的船舶横向运动姿态与受扰Kalman滤波的统计建模[J].船舶力学,2007,11(5): 702-707页
[237]唐慧妍.船舶横向运动受扰估计、建模及LQG控制研究[D]..哈尔滨工程大学博士学位论文2005: 185-186页
[238]孙宏放.基于斜舵船舶减横摇LQG控制研究[J].系统仿真学报. 2009,21(2): 503-506页
[239]郝燕玲,徐耀群,罗跃生,沈继红.最优化方法[M].哈尔滨工程大学出版社. 2001: 95-101页
[240] Shi Y, Eberhart R, A modified particleswarm optimizer[C]. In: Proc. Of the IEEE CEC. 1998: 69-73P
[241] Beck J V, Arnold K J. Parameter Estimation in Engineering and Science [M]. New York: Wiley, 1977.
[242] Saltelli A,Ratto M, Tarantola S,et al. Sensitivity analysis practices: Strategies for model-based inference [J]. Reliability Eneineering & System Safety, 2006, 91(10-11): 1109-1125P
[243] Debabrata Sen. A Study on sensitivity of maneuverability Performance on the hydrodynamic coeffieients for submerged bodies [J]. Joumal of ShiP Reseaeh, 2000, 44(3):186-196P
[244]胡坤,徐亦凡,王树宗.基于水动力系数敏感性指数的水下航行器运动方程简化研究[J].武汉理工大学学报:交通科学与工程版, 2008, 3(2): 358-361页
[245]王燕飞,朱军,张振山.评估水动力系数对潜艇操纵性影响的一种方法[J].船舶力学, 2005,9(5):61-68页
[246] Richter, G.M., et al., Sensitivity analysis for a complex crop model applied to Durum wheat in the Mediterranean [J]. Eur. J. Agron. (2009), doi: 10.1016 j.eja. 2009.09.002
[247] Doug Perrault, Neil Bose. Sensitivity of AUV added mass coefficients to variations in hull and control plane geometry [J]. Ocean Engineering 30 (2003), 645-671P