粒子群优化算法及其在机电设备中的应用研究
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摘要
为适应机电设备智能化、自动化和高可靠性的发展,现代机电设备无论是机械系统还是控制系统都越来越复杂,客观上对机电设备的设计、状态监测和控制方法等都提出了更高的要求,其中的许多问题都可以转化为优化问题用优化算法进行求解。作为一种基于群体智能的随机优化算法,粒子群优化(Particle Swarm Optimization,PSO)算法以其简单、易于实现和快速的收敛性能,在工程优化实践中得到广泛应用。本文在对粒子群优化算法基本理论进行研究的基础上,提出了一种改进的粒子群优化算法,并探讨了其在机电设备的设计、状态监控和控制中的应用。主要研究内容包括以下几个方面:
(1)阐述了优化的一般数学模型以及粒子群优化算法产生的背景和研究现状。分析了粒子群优化算法的基本原理和特点,并对基本粒子群优化算法中参数对算法性能的影响进行了简单分析。
(2)针对基本粒子群优化算法存在容易陷入局部最优的问题,基于生物群体的认知和决策过程,提出了一种基于“维信息共享(Dimension Information Sharing)”和“动态认知(Dynamic Cognition)”的改进粒子群优化算法(DDPSO)。在速度更新中随机选取粒子某一维的信息复制到其他维,实现“各维之间信息的共享”;同时,为适应不同的优化问题,引入动态认知思想,每一次迭代过程中,针对“认知”的不同阶段,用不同的更新策略对种群中的粒子及粒子的个体最优和整个种群全局最优进行更新。DDPSO算法模拟生物群体的认知过程,将生物群体在不同阶段的认知过程转换为不同的更新策略,从模型结构上对粒子群优化算法进行了改进,使算法更加符合生物群体的思维过程及其社会属性,发挥了不同更新策略的优势,更加能够体现生物群体的智能。通过对标准测试函数集中反映算法不同性能的测试函数的测试、神经网络的训练以及混沌控制系统控制参数的优化,证明了DDPSO算法具有非常好的优化能力。
(3)针对齿轮箱故障状态识别中,BP算法存在的问题(过多的参数需要调节,收敛速度慢,容易陷入局部最优等)和基本粒子群存在的问题,将DDPSO算法引入到了齿轮箱的故障诊断中用于神经网络的训练。以BP神经网络的权值和阈值作为粒子的位置矢量,圴方误差作为粒子群的适应度值,通过粒子群优化算法对权值和阈值进行调整、优化。诊断结果表明,DDPSO算法与BP、PSO-TVIWD、PSO-TVACD和PSO-DV算法相比,更容易跳出局部最优,且具有更高的诊断精度和收敛性能,提高了齿轮箱故障诊断率,为复杂非线性机械系统的故障诊断和状态监测提供了新的思路和方法。
(4)在对火炮身管热护套防护效率测试系统及现场测试数据进行分析的基础上,建立了测试系统热辐射参数调节的数学模型,将热辐射参数的调节转化为组合优化问题,并利用离散二进制粒子群优化算法对该组合优化问题进行求解。同时,探讨了粒子群优化算法在实时控制系统中存在的问题,提出了一种修正方法,弥补了算法的缺陷。结果表明,该方法不仅实现了热辐射参数的自动调节和测试过程的自动化,而且满足了自动控制系统在实时性、稳定性和准确性方面的要求。
(5)针对复杂机电设备电气控制系统设计过程中,电气配线工作主要由设计人员手工依靠经验完成,设计周期长,容易出现错误的问题,将电气配线转化为组合优化问题,研究了利用粒子群算法实现复杂电气控制系统自动配线的方法。主要研究了以下3点:1)电气配线问题数学模型的建立方法;2)粒子群优化算法求解电气配线数学模型的理论、方法和步骤;3)结合科研项目实例及第3章的DDPSO算法对该方法进行了实验验证。结果表明,该方法能够实现电气配线的智能化、自动化,达到缩短设计周期,降低出错概率的目的,为复杂机电设备电气控制系统的自动设计提供了一种新的思路和方法,对提高我国装备制造业信息化和自动化水平具有一定的意义。
With the development of high automatization, intellectuality and reliability, the modern electromechanical equipments are becoming more and more complicated in both mechanical and control systems. Therefore, the higher standards are brought up in the equipment designing, the status monitoring and the system controlling, etc, where many of problems can be regarded as the optimization ones and be solved by the socalled optimization algorithms. The Particle Swarm Optimization (PSO) algorithm, as a population-based and heuristic optimization technique, has been successfully applied to optimize a variety of optimization problems in engineering practice because of the simple and rapid convergence speed, and easy implemention. In this thesis, an improved PSO (DDPSO) algorithm is proposed under the theory analysis of the PSO algorithm, and then its applications in the design, the status monitoring and the control field of electromechanical equipments are discussed too. The main works in this thesis are listed as follows.
(1) The general mathematical model of optimization problems and the PSO algorithm are introduced firstly,including the research background and present status, the basic principle and main characteristics, and the influence of parameter on the convergence performance of the basic PSO algorithm.
(2) To improve performance and avoid trapping to local optimum, a modified PSO algorithm (DDPSO) is proposed based on Dimension Information Share and Dynamic Cognition in the animal cognition and the decision-making process. One dimension of the particle is selected randomly and copied to other dimensions when updating the velocity of the particle. Meanwhile, DDPSO simulates the animal cognition in every running, and the different cognitive stages are represented by the respective velocity formula, in order to adapt for different optimization problems. Therefore, it is an immense improvement in model structure of PSO and more accordant to cognitive process, social nature and swarm intelligence of animal. The effectiveness and practicability are demonstrated by the simulation results in testing with benchmark functions, training of back-propagation neural network (BPNN) and optimizing parameters in the chaos systems.
(3) BPNN becomes more and more popular in fault diagnosis, especially in the field of gear-boxes. Unfortunately, the back-propagation algorithm is a gradient-based method, where some inherent problems are frequently encountered when using this algorithm, such as slow convergence speed in training, easiness to trap into a local minimum, etc. The DDPSO algorithm is used to train BP neural network in fault diagnosis of the gear box. Each particle position vector is made up of all of the weights and thresholds in BPNN, and the minimum sum of mean square error (MSE) is treated as the fitness of DDPSO. The experimental results verified that DDPSO, as the intelligent method, can escape from local minimum and has faster convergence than back-propagation, PSO-TVIWD, PSO-TVACD and PSO-DV in training BPNN. DDPSO can achieve quite high accuracy rate of recognition and provide a new way in fault diagnosis of the complicated non-line mechanical system.
(4) Based on the testing system for protective effect evaluation of the thermal protection jacket and field data, the mathematical model to adjust the thermal protection jacket is established. As an essentially a typical combinational optimization problem, a discrete Binary Particle Swarm Optimization (BPSO) algorithm is developed to solve this problem. Furthermore, with discussing BPSO in real-time control system, the rectificating error method is proposed to overcome the shorcomings of BPSO. Experimental result shows that the presented algorithm can not only achieve the automatic adjustment of the thermal radiation and automation of the testing process, but also meet the requirement of rapidity, stablity and accuracy for the control system.
(5) In the traditional design of electrical control system, the distribution cabinet is usually made manually by professional engineers, who expert a great influence on the quality of the job depends mainly on the experience of engineers, such as high costs, long development cycles and frequent errors. Aimed to the questions above, a new method is proposed to do this work automatically based on PSO. Firstly, the mathematical model of the distribution cabinet is established. Secondly, the methods and steps of PSO to solve this problem are discussed. And finally, the proposed method and DDPSO are experimented in a real application of automatic welding equipment for wire mesh. Experiment results prove the proposed method is fast and effective, can accomplish the intelligent and automatic design of the distribution cabinet, and can also shorten the design cycle time and reduce the design error risks. This work provides a new way to achieve automatic distribution cabinet design for the complicated electrical control system, and it can be highly significant to raise the information and automation level in equipment manufacturing industries.
(1)阐述了优化的一般数学模型以及粒子群优化算法产生的背景和研究现状。分析了粒子群优化算法的基本原理和特点,并对基本粒子群优化算法中参数对算法性能的影响进行了简单分析。
(2)针对基本粒子群优化算法存在容易陷入局部最优的问题,基于生物群体的认知和决策过程,提出了一种基于“维信息共享(Dimension Information Sharing)”和“动态认知(Dynamic Cognition)”的改进粒子群优化算法(DDPSO)。在速度更新中随机选取粒子某一维的信息复制到其他维,实现“各维之间信息的共享”;同时,为适应不同的优化问题,引入动态认知思想,每一次迭代过程中,针对“认知”的不同阶段,用不同的更新策略对种群中的粒子及粒子的个体最优和整个种群全局最优进行更新。DDPSO算法模拟生物群体的认知过程,将生物群体在不同阶段的认知过程转换为不同的更新策略,从模型结构上对粒子群优化算法进行了改进,使算法更加符合生物群体的思维过程及其社会属性,发挥了不同更新策略的优势,更加能够体现生物群体的智能。通过对标准测试函数集中反映算法不同性能的测试函数的测试、神经网络的训练以及混沌控制系统控制参数的优化,证明了DDPSO算法具有非常好的优化能力。
(3)针对齿轮箱故障状态识别中,BP算法存在的问题(过多的参数需要调节,收敛速度慢,容易陷入局部最优等)和基本粒子群存在的问题,将DDPSO算法引入到了齿轮箱的故障诊断中用于神经网络的训练。以BP神经网络的权值和阈值作为粒子的位置矢量,圴方误差作为粒子群的适应度值,通过粒子群优化算法对权值和阈值进行调整、优化。诊断结果表明,DDPSO算法与BP、PSO-TVIWD、PSO-TVACD和PSO-DV算法相比,更容易跳出局部最优,且具有更高的诊断精度和收敛性能,提高了齿轮箱故障诊断率,为复杂非线性机械系统的故障诊断和状态监测提供了新的思路和方法。
(4)在对火炮身管热护套防护效率测试系统及现场测试数据进行分析的基础上,建立了测试系统热辐射参数调节的数学模型,将热辐射参数的调节转化为组合优化问题,并利用离散二进制粒子群优化算法对该组合优化问题进行求解。同时,探讨了粒子群优化算法在实时控制系统中存在的问题,提出了一种修正方法,弥补了算法的缺陷。结果表明,该方法不仅实现了热辐射参数的自动调节和测试过程的自动化,而且满足了自动控制系统在实时性、稳定性和准确性方面的要求。
(5)针对复杂机电设备电气控制系统设计过程中,电气配线工作主要由设计人员手工依靠经验完成,设计周期长,容易出现错误的问题,将电气配线转化为组合优化问题,研究了利用粒子群算法实现复杂电气控制系统自动配线的方法。主要研究了以下3点:1)电气配线问题数学模型的建立方法;2)粒子群优化算法求解电气配线数学模型的理论、方法和步骤;3)结合科研项目实例及第3章的DDPSO算法对该方法进行了实验验证。结果表明,该方法能够实现电气配线的智能化、自动化,达到缩短设计周期,降低出错概率的目的,为复杂机电设备电气控制系统的自动设计提供了一种新的思路和方法,对提高我国装备制造业信息化和自动化水平具有一定的意义。
With the development of high automatization, intellectuality and reliability, the modern electromechanical equipments are becoming more and more complicated in both mechanical and control systems. Therefore, the higher standards are brought up in the equipment designing, the status monitoring and the system controlling, etc, where many of problems can be regarded as the optimization ones and be solved by the socalled optimization algorithms. The Particle Swarm Optimization (PSO) algorithm, as a population-based and heuristic optimization technique, has been successfully applied to optimize a variety of optimization problems in engineering practice because of the simple and rapid convergence speed, and easy implemention. In this thesis, an improved PSO (DDPSO) algorithm is proposed under the theory analysis of the PSO algorithm, and then its applications in the design, the status monitoring and the control field of electromechanical equipments are discussed too. The main works in this thesis are listed as follows.
(1) The general mathematical model of optimization problems and the PSO algorithm are introduced firstly,including the research background and present status, the basic principle and main characteristics, and the influence of parameter on the convergence performance of the basic PSO algorithm.
(2) To improve performance and avoid trapping to local optimum, a modified PSO algorithm (DDPSO) is proposed based on Dimension Information Share and Dynamic Cognition in the animal cognition and the decision-making process. One dimension of the particle is selected randomly and copied to other dimensions when updating the velocity of the particle. Meanwhile, DDPSO simulates the animal cognition in every running, and the different cognitive stages are represented by the respective velocity formula, in order to adapt for different optimization problems. Therefore, it is an immense improvement in model structure of PSO and more accordant to cognitive process, social nature and swarm intelligence of animal. The effectiveness and practicability are demonstrated by the simulation results in testing with benchmark functions, training of back-propagation neural network (BPNN) and optimizing parameters in the chaos systems.
(3) BPNN becomes more and more popular in fault diagnosis, especially in the field of gear-boxes. Unfortunately, the back-propagation algorithm is a gradient-based method, where some inherent problems are frequently encountered when using this algorithm, such as slow convergence speed in training, easiness to trap into a local minimum, etc. The DDPSO algorithm is used to train BP neural network in fault diagnosis of the gear box. Each particle position vector is made up of all of the weights and thresholds in BPNN, and the minimum sum of mean square error (MSE) is treated as the fitness of DDPSO. The experimental results verified that DDPSO, as the intelligent method, can escape from local minimum and has faster convergence than back-propagation, PSO-TVIWD, PSO-TVACD and PSO-DV in training BPNN. DDPSO can achieve quite high accuracy rate of recognition and provide a new way in fault diagnosis of the complicated non-line mechanical system.
(4) Based on the testing system for protective effect evaluation of the thermal protection jacket and field data, the mathematical model to adjust the thermal protection jacket is established. As an essentially a typical combinational optimization problem, a discrete Binary Particle Swarm Optimization (BPSO) algorithm is developed to solve this problem. Furthermore, with discussing BPSO in real-time control system, the rectificating error method is proposed to overcome the shorcomings of BPSO. Experimental result shows that the presented algorithm can not only achieve the automatic adjustment of the thermal radiation and automation of the testing process, but also meet the requirement of rapidity, stablity and accuracy for the control system.
(5) In the traditional design of electrical control system, the distribution cabinet is usually made manually by professional engineers, who expert a great influence on the quality of the job depends mainly on the experience of engineers, such as high costs, long development cycles and frequent errors. Aimed to the questions above, a new method is proposed to do this work automatically based on PSO. Firstly, the mathematical model of the distribution cabinet is established. Secondly, the methods and steps of PSO to solve this problem are discussed. And finally, the proposed method and DDPSO are experimented in a real application of automatic welding equipment for wire mesh. Experiment results prove the proposed method is fast and effective, can accomplish the intelligent and automatic design of the distribution cabinet, and can also shorten the design cycle time and reduce the design error risks. This work provides a new way to achieve automatic distribution cabinet design for the complicated electrical control system, and it can be highly significant to raise the information and automation level in equipment manufacturing industries.
引文
[1]王凌,刘波.微粒群优化与调度算法.北京:清华大学出版社,2008:35-39.
[2]计时鸣.机电一体化控制技术与系统.陕西:西安电子科技大学出版社,2009:1-2.
[3] Kennedy James F,Eberhart Russell and Shi Yuhui.Swarm Intelligence. Elsevier Science Ltd,2005.
[4] Kennedy J,Eberhart R.C.Particle Swarm Optimization.In:IEEE International Conference on Neural Networks,IV. Piscataway,NJ:IEEE Service Center,1995:1942-1948.
[5]李宁.粒子群优化算法的理论分析与应用研究.博士学位论文,华中科技大学,2006.
[6]宋胜利.混合粒子群协同优化算法及其应用研究.博士学位论文,华中科技大学,2006.
[7] Reeves C.R.Modern Heuristic Techniques for Combinatorial Problems.Oxford: Blackwell Scientific Publications:1993.
[8] Fogel D.B.Evolutionary Computation:Toward a New Philosophy of Machine Intelligence.NewYork:Wiley-IEEE Press,1995.
[9] Colomi A , Dorigo M , ManiezzoV et al . Distributed optimization by ant colonies.Proceedings of the lst European Conference on Artificial Life,1991,134-142.
[10] Dorigo M.Optimization, leaming and natural algorithms.Ph.D. Disseration,Department of Electronics,Politecnico diMilano,Italy,1992.
[11]李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式:鱼群算法.系统工程理论与实践,2002,22.11:32-38.
[12] D.Karaboga.An Idea Based On Honey Bee Swarm For Numerical Optimization, Technical Report-TR06,Erciyes University, Engineering Faculty, Computer Engineering Department,2005.
[13]周永华,毛宗源.一种新的全局优化搜索算法——人口迁移算法.华南理工大学学报(自然科学版),2003,31.3:l-5.
[14] E.Bonabeau,M.Dorigo,G.Theraulaz.Swarm Intelligence:From Natural to ArtificialSystems.New York:Oxford University Press,1999.
[15] Wolpert.D.H and Macteady W.G.. No free lunch theorems for optimization. IEEE Transaction on Evolutionary Computation,xl(l),2005,67-82.
[16] Reynolds C.W, Flocks Herds and Schools.A Distributed Behavioral Model.Computer Graphics,1987,21:4:25-34.
[17] Clerc M,Kennedy J.The particle swarm:explosion, stability, and convergence in multi-dimensional complex space.IEEE Transaction on Evolutionary Computation,2002, 6.1:58-73.
[18] Ozcan E,Mohan C.Particle Swarm Optimization:Surfing the Waves.In:Proceedings of the Congress on Evolutionary Computation,1999:1939-1944.
[19] Frans van den Bergh,A P Engelbrech.A New Locally Convergent Particle Swarm Optimizer.In:Proceedings of the IEEE International Conference on Systems,Man and Cybernetics,2002,Vol.3:94-99.
[20] Solis F,Wets R.Minimization by Random Search Techniques.Mathematics of Operations Research,1981,6:19-30.
[21] Bergh van den F.An analysis of particle swarm optimizers.Ph.D.dissertation, Department of Computer Science:University of Pretoria,Pretoria,2001.
[22]王俊年.微粒群算法及其在锌电解整流供电系统优化中的应用研究.博士学位论文.中南大学,2007.
[23] Shi Y and Eberhart RC,A Modified Particle Swarm Optimiser.IEEE International Conference on Evolutionary Computation (1998), Anchorage,Alaska, May 4-9:34-43.
[24] Shi Y and Eberhart RC.Fuzzy Adaptive Particle Swarm Optizimation.In Proceedings of the Congress on Evolutionary Computation 2001,Seoul,Korea,IEEE Service Center,2001: 101-106.
[25] Shi Y and Eberhart RC.Comparing Inertia Weights and Constriction Factro in Particle Swarm Optizimation.In Proceedings of the IEEE International Congress on Evolutionary Computation,vol. 1:84-88.
[26] Ratnaweera A,Halgamuge SK,and Watsom HC.Self-Organizing Hierarchical ParticleSwarm Optimizer with Time-Varying Acceleration Coefficients, IEEE Transactions on Evolutionary Computation.2004,8.3:240-255.
[27] Kennedy J,R Mendes.Topological Structure and Particle Swarm Performance.The 2002 Congress on Evolutionary Computation.Piscataway.IEEE Service Center:2002:1671-1676.
[28] Mendes R,Kennedy J.The Full Informed Particle Swarm:simpler,maybe better.IEEE TRANSACTION ON EVOLUTIONARY COMPUTATION,2004,8.3:204-210.
[29] Mendes R.Population Topologies and Their Influence in Particle Swarm Performance. PHD Dissertation.Department of Information,University of Minho,Braga,Portugal.2004
[30] Brits.R.,A.P.Engelbreeht,and F. Vanden Bergh. A Niching Partiele Swarm Optimiler . In Proeeedings of the Conference on Simulated Evolution and Learning. November,2002, SingaPore.
[31]贾东立,张家树.基于混沌变异的小生境粒子群算法.控制与决策.2007,22.1:117-120.
[32]庞巍,王康平,周春光,黄岚,季晓辉.模糊离散粒子群优化算法求解旅行商问题.小型微型计算机系统.2005,26.8:1331-1334.
[33]周雅兰,王甲海,印鉴.一种基于分布估计的离散粒子群优化算法.电子学报.2008,36.6:1242-1248.
[34]叶东毅,廖建坤.最小约简问题的一个免疫离散粒子群算法.小型微型计算机系统.2008,29.6:1089-1092.
[35] SUN J,FENG B, XUW. Particle Swarm Optimization with Particle Having Quantum Behavior.IEEE Proc. of Congress on Evolutionar Computation.2004:325-331.
[36] Yang S,Wang M,Jiao L.A quantum particle swarm optimization.Proceeding of the IEEE Congress on Evolutionary Computation,2004:320-324.
[37] S.Das ectal.Particle Swarm Optimization and Differential Evolution Algorithms: Technical Analysis, Applications and Hybridization Prespectives.Studies in Computional Intelligence.2008,116:1-38.
[38] Yi-Tung Kao,Erwie Zahara.A hybrid genetic algorithm and particle swarm optimization for multimodal functions.Applied Soft Computing.2008,8:849-857.
[39]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.
[40] P.S.Shelokar,Patrick Siarry, V.K.Jayaraman, B.D.Kulkarni.Particle swarm and ant colony algorithms hybridized for improved continuous optimization.Applied Mathematics and Computation. 2007,188:129-142.
[41]高鹰,谢胜利.混沌粒子群优化算法.计算机科学.2004,31.8:13-15.
[42] Bo Liu,Hongxia Pan.A Hybrid PSO-DV Based Intelligent Method for Fault Diagnosis of Gear-box.2009 IEEE International Symposium on Computational Intelligence in Robotics and Automation.2009:236-242.
[43] Liu Bo, Pan Hongxia. Fault Diagnosis of Bearing Using Wavelet Packet Transform and PSO-DV Based neural network . The 6th International Conference on Natural Computation.2010:1127-1133.
[44] Wei Xiuye, Pan Hongxia and Ma Qingfeng. Application of Particle Swarm Optimization Based Neural Network to Fault Diagnosis.Journal of Vibration,Measurement & Diagnosis.2006,26:133-137.
[45] Liu Bo,Pan Hongxia.FAULT DIAGNOSIS OF BEARING USING PSO WITH DIFFERENTIAL OPERATOR AND NEURAL NETWORK . The 9th International Conference on Foundations and Applications of Computational Intelligence.2010:467-473.
[46] Qian-jin Guo,Hai-bin.A hybrid PSO-GD based intelligent method for machine diagnosis.Digital Signal Processing.2006,16:402-410.
[47]车林仙,何兵,易建,陈长忆,罗佑新.对称结构Stewart机构位置正解的改进粒子群算法.农业机械学报.2008,39.10:158-163.
[48]曾议,竺长安.基于群智能算法的设备布局离散优化研究.计算机集成制造系统.2007,13.3:541-552.
[49]方红庆.一种改进粒子群算法及其在水轮机控制器PID参数优化中的应用.南京理工大学学报(自然科学版).2008,32.3:274-277.
[50]赵波,曹一家.电力系统无功优化的多智能体粒子群优化算法.中国电机工程学报.22008,25.5:1-7.
[51]韩恺,赵均,钱积新.电力系统机组组合问题的闭环粒子群算法.电力系统及自动化.2009,33.1:36-69.
[52]余欣梅,李妍,熊信良,吴耀武.基于PSO考虑谐波影响的补偿电容器优化配置.中国电机工程学报.2003,23. 2:26-120
[53]熊伟丽,徐保国.基于PSO-SVR的发酵过程状态预估模型.南京理工大学学报(自然科学版).2008,32.4:517-521.
[54]孔力,程晶晶,宋胜利,苏日建.基于改进粒子群优化技术的拜耳法物料平衡计算.华中科技大学学报(自然科学版).2008,36.1:95-98.
[55]秦元庆,孙德宝,李宁,马强.基于粒子群算法的移动机器人路径规划.机器人.2004, 26.5:222-225.
[56]薛英花,田国会,李国栋.利用改进的粒子群算法的机器人全局路径规划,华中科技大学学报(自然科学版).2008,36.S1期:167-170
[57]邓高峰,张雪萍,刘彦萍.一种障碍环境下机器人路径规划的蚁群粒子群.控制理论与应用.2009,26.8:879-883.
[58]翟学敏,刘渊,刘波,毕蓉蓉.改进的XML智能数据清洗策略.计算机工程.2009,35.4:66-71.
[59]唐贤伦,庄陵,李银国,曹长修.基于粒子群优化和模糊c均值聚类的入侵检测.计算机工程.2008,34.4:13-15.
[60]张倩,段高燕,张晓光,蒋达娅.PSO算法用于WDM系统多信道PMD补偿的研究.光子学报.2008,37.9:1829-1832.
[61]龙银芳,赵知劲,赵治栋.基于离散粒子群算法的MC-CDMA多用户检测.压电与声光.2009,31.3:305-308.
[62]刘洪武,冯全源.分集接收的STBC-MC-CDMA系统中基于PSO算法的多用户检测.电子与信息学报.2009,31.1: 45-48
[63]丁英强,孙雨耕,李婷雪.基于时分频分的无线传感器分簇网络MAC层协议.天津大学学报.2008,41.8:904-910.
[64] Shi Y,Ebethart R.C.A modified Optimizer.Proceedings of the IEEE Congresson Evolutionary Computation.Piseataway,1998:69-73.
[65] Y.Shi,R.C.Eberhart.Parameter selection in particle swarm optimization,Evolutionary Programming VII.Lecture Notes in Computer Science.1998,1447:591-600.
[66] Y.Shi,R.C.Eberhart.Fuzzy adaptive particle swarm optimizer.Proceedings of the IEEE Congress on Evolutionary Computation.IEEE Service Center:2001:87-93.
[67] Kennedy J.Small Worlds and Mega-Minds:Effects of Neighborhood Topology on Particle Swarm Performance . Proceedings of the 1999 Congress of Evolutionary Computation,vol. 3. IEEE Press: New York:1931-1938.
[68] Vanden Bergh F,Engelbrecht A.A Cooperative Approach to Particle Swarm Optimization,.IEEE Transactions on Evolutionary Computation.2004,8.3:89-95.
[69] A. Ratnaweera,S.K.Halgamuge.Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficient . IEEE Transaction on Evolutionary Computation .2004,8.3:240-255.
[70] K.C.Tan , E.J.Teoh , Q.Yua,K.C.Goh. A hybrid evolutionary algorithm for attribute selection in data mining.Expert Systems with Applications. 2009,36:8616-8630.
[71]曾建潮,介倩,崔志华.微粒群算法.北京:科学出版社,2004.
[72] Kennedy J,Eberhart R.C.A Discrete Binary Version of the Particle Swarm Algorithm.The 1997 Conference on System, Cybernetics and Informatics.Piscataway: 1997:4104-4109.
[73]魏秀业.基于粒子群优化的齿轮箱智能故障诊断研究.博士学位论文.中北大学,2009,6.
[74]理查德.格里格,菲利普.津巴多(著),王垒等译.心理学与生活,北京:人民邮电出版社,2003,10.
[75]纪震,廖惠连,吴青华.粒子群算法及应用.北京:科学出版社,2009,1.
[76] Boyd R,Recharson P.Culture and the evolutionary process.Chicago:University of Chicago Press,1985.
[77] Marcin Molga , Czes?aw Smutnicki . Test functions for optimization needs.http://www.zsd.ict.pwr.wroc.pl/files/docs/functions.pdf.
[78] J.J.Liang,A.K.Qin,Ponnuthurai Nagaratnam Suganthan,S.Baskar.Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodal Functions.IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION.2006,10.3:281-295
[79] Qi Kang,Lei Wang,Qi-di Wu.A novel ecological particle swarm optimization algorithm and its population dynamics analysis.Applied Mathematics and Computation. 2008,205:61-72.
[80] Chia-Nan Ko,Ying-Pin Chang.An orthogonal-array-based particle swarm optimizerwith nonlinear time-varying evolution.Applied Mathematics and Computation.2007,191:272-279.
[81]颜玉玲.滚动轴承的振动监测及故障诊断.机械研究与应用.1998.
[82]葛哲学,孙志坚.神经网络理论与MATALAB R2007实现.北京:电子工业出版社,2007
[83] Chen DeBao,Zhao ChunXia.Particle swarm optimization with adaptive population size and its application.Applied Soft Computing.2009,9:39-48.
[84]钟秉林,黄仁.机械故障诊断学.北京:机械工业出版社,2002.
[85]鄂加强.智能故障诊断及其应用.长沙:湖南大学出版社,2006,12.
[86]于德介,程军胜等.Hilbert能量谱及其在齿轮箱故障诊断中的应用.湖南大学学报,2003,30. 4:47-50.
[87] CHENG Jun-Sheng,YU De-Jie,YANG Yu.A method for gear fault diagnosis based on empirical mode decomposition.International Journal of Plant Engineering and Management,2004,9.4:230-235.
[88]栾军英,康海英等.基于阶次跟踪和BP网络的齿轮箱故障模式识别.军械工程学院学报.2006,18.2:20-23.
[89]田昊,栾军英等.阶次跟踪分析在齿面磨损故障诊断中的应用[J].军械工程学院学报,2005,17(5):57-60.
[90]盛兆顺,尹琦岭.设备状态监测与故障诊断技术及应用.北京:化学工业出版社.2003,6.
[81]杨军,冯振声,黄考利.装备智能故障诊断技术.北京:国防工业出版社.2004.8
[92]潘宏侠,姚竹亭.齿轮传动系统状态检测与故障诊断.华北工学院学报,2001,22.4:313-318.
[93]丁康.传动箱齿轮与轴故障的振动方法研究.冲击与振动.1994,13.2:26-32.
[94]韩捷.齿轮故障的振动频谱机理研究.机械传动.1997,12:34-37.
[95] JIA Rong,XU Qi-hui,LI Hui ,LIU Wei.Power Transformer Fault Diagnosis via Neural Network Based on Particle Swarm Optimization with Neighborhood Operator.High Voltage Apparatus.2008,44:8-19.
[96] DING Jing-guo1,JIAO Jing-min1,ZANPei. Hot Strip Width Prediction During Rough Rolling with PSO-Neural Network Based on Fuzzy Clustering.Journal of Northeastern University(NaturalScience).2007,28:1282-1292.
[97] Jialin Zhou.PSO-based neural network optimization and its utilization in a boring machine.Journal of Materials Processing Technology.2006,178:19-23.
[98] Yi Daa,Ge Xiurun.An improved PSO-based ANN with simulated annealing technique.Neurocomputing.2005,63 .10:527-533
[99] Qian-jin Guo,Hai-bin.A hybrid PSO-GD based intelligent method for machine diagnosis.Digital Signal Processing.2006,16:402-411.
[100]潘旭峰等.齿轮轴承故障诊断中特征参数敏感性分析.兵工学报.1997,18.4: 294-297.
[101] Jian-Da Wu,Chuang-Chin Hsu.Fault gear identification using vibration signal with discrete wavelet transform technique and fuzzy-logic inference.Expert Systems with Applications.2009,36:3785-3794.
[102]胡寿松.自动控制原理(第四版).北京:科学出版社,2001,2
[103] Dong Hwa Kim.GA–PSO based vector control of indirect three phase induction motor.Applied Soft Computing. 2007,7:601-611.
[104] U.Zuperl,F.Cus, M.Reibenschuh.Neural control strategy of constant cutting force system in end milling.Robotics and Computer-Integrated Manufacturing.2011,27:485-493
[105] Hongqing Fang,Long Chen,Zuyi Shen.Application of an improved PSO algorithm to optimal tuning of PID gains for water turbine governor.Energy Conversion and Management2011,52:1763-1770.
[106] Majid Zamani,Masoud Karimi-Ghartemani,Nasser Sadati,Mostafa Parniani.Design of a fractional order PID controller for an AVR using particle swarm optimization.Control Engineering Practice.2009,17:1380-1387.
[107]谈乐斌,张相炎,管红根,等.火炮概论.北京:北京理工大学出版社,2005.
[108]杨惠勇,张培林,阎鹏程,等.火炮身管弯曲度检测方法研究.兵工自动化,2008,27.9:19-21.
[109]赵剑,谢宗蕻,张磊.高温合金热防护系统设计与分析.宇航学报,2008,29.5:28-32.
[110]罗来科,宣益民,韩玉阁.坦克炮管温度场的有限元计算.兵工学报,2005,26.1:6-9.
[111] Yi-Xiong Jin,Hao-Zhong Cheng,Jian-yong Yan,Li Zhang.New discrete method for particle swarm optimization and its application in transmission network expansion planning. Electric Power Systems Research.2007,77:227-233.
[112] Ching-Jong Liao,Chao-Tang Tseng,Pin Luarn.A discrete version of particle swarm optimization for flowshop scheduling problems.Computers & Operations Research,2007,34:3099-3111.
[113] Bin Jiao,Zhigang Lian,Xingsheng Gu.A dynamic inertia weight particle swarm optimization algorithm.Chaos, Solitons and Fractals.2008,37:698-705.
[114]君兰工作室.机电一体化-从原理到应用.北京,科学出版社:2009
[115] Hongcheng Liu,Liang Gao,Quanke Pan.A hybrid particle swarm optimization with estimation of distribution algorithm for solving permutation flowshop scheduling problem. Expert Systems with Applications.2010,43:9645-9653.
[116] M. Fatih Tasgetiren,Yun-Chia Liang,Mehmet Sevkli,Gunes Gencyilmaz.A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem.European Journal of Operational Research.2007,177:1930-1947.
[117] B . Naderia , RubénRuizb . The distributed permutation flowshop schedulingproblem.Computers & Operations Research.2010,37 :754-768.
[118] Zhigang Lian,Xingsheng Gu,Bin Jiao.A similar particle swarm optimization algorithm for permutation flowshop scheduling to minimize makespan.Applied Mathematics and Computation.2006,175:773-785.
[119] Bo Liu,Ling Wang,Yi-Hui Jin.An Effective PSO-Based Memetic Algorithm for Flow Shop Scheduling . IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS.2007,37:18-27.
[120] Bean J C.Genetic algorithm and random keys for sequencing and optimization.ORSA Journal of Computing.1994,6.2:154-160.
[121]李志耕,黄佩君,赵以裕,马长瀛.GB50171-1992,1992.电气装置安装工程盘、柜及二次回路结线施工及验收规范.
[122]中国工程建设标准化协会.CECS 49-1993,1993.低压成套开关设备验收规范.
[123] Nawaz M,Enscore E,Ham I.A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem.Omega.1983,11:91-95.
[2]计时鸣.机电一体化控制技术与系统.陕西:西安电子科技大学出版社,2009:1-2.
[3] Kennedy James F,Eberhart Russell and Shi Yuhui.Swarm Intelligence. Elsevier Science Ltd,2005.
[4] Kennedy J,Eberhart R.C.Particle Swarm Optimization.In:IEEE International Conference on Neural Networks,IV. Piscataway,NJ:IEEE Service Center,1995:1942-1948.
[5]李宁.粒子群优化算法的理论分析与应用研究.博士学位论文,华中科技大学,2006.
[6]宋胜利.混合粒子群协同优化算法及其应用研究.博士学位论文,华中科技大学,2006.
[7] Reeves C.R.Modern Heuristic Techniques for Combinatorial Problems.Oxford: Blackwell Scientific Publications:1993.
[8] Fogel D.B.Evolutionary Computation:Toward a New Philosophy of Machine Intelligence.NewYork:Wiley-IEEE Press,1995.
[9] Colomi A , Dorigo M , ManiezzoV et al . Distributed optimization by ant colonies.Proceedings of the lst European Conference on Artificial Life,1991,134-142.
[10] Dorigo M.Optimization, leaming and natural algorithms.Ph.D. Disseration,Department of Electronics,Politecnico diMilano,Italy,1992.
[11]李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式:鱼群算法.系统工程理论与实践,2002,22.11:32-38.
[12] D.Karaboga.An Idea Based On Honey Bee Swarm For Numerical Optimization, Technical Report-TR06,Erciyes University, Engineering Faculty, Computer Engineering Department,2005.
[13]周永华,毛宗源.一种新的全局优化搜索算法——人口迁移算法.华南理工大学学报(自然科学版),2003,31.3:l-5.
[14] E.Bonabeau,M.Dorigo,G.Theraulaz.Swarm Intelligence:From Natural to ArtificialSystems.New York:Oxford University Press,1999.
[15] Wolpert.D.H and Macteady W.G.. No free lunch theorems for optimization. IEEE Transaction on Evolutionary Computation,xl(l),2005,67-82.
[16] Reynolds C.W, Flocks Herds and Schools.A Distributed Behavioral Model.Computer Graphics,1987,21:4:25-34.
[17] Clerc M,Kennedy J.The particle swarm:explosion, stability, and convergence in multi-dimensional complex space.IEEE Transaction on Evolutionary Computation,2002, 6.1:58-73.
[18] Ozcan E,Mohan C.Particle Swarm Optimization:Surfing the Waves.In:Proceedings of the Congress on Evolutionary Computation,1999:1939-1944.
[19] Frans van den Bergh,A P Engelbrech.A New Locally Convergent Particle Swarm Optimizer.In:Proceedings of the IEEE International Conference on Systems,Man and Cybernetics,2002,Vol.3:94-99.
[20] Solis F,Wets R.Minimization by Random Search Techniques.Mathematics of Operations Research,1981,6:19-30.
[21] Bergh van den F.An analysis of particle swarm optimizers.Ph.D.dissertation, Department of Computer Science:University of Pretoria,Pretoria,2001.
[22]王俊年.微粒群算法及其在锌电解整流供电系统优化中的应用研究.博士学位论文.中南大学,2007.
[23] Shi Y and Eberhart RC,A Modified Particle Swarm Optimiser.IEEE International Conference on Evolutionary Computation (1998), Anchorage,Alaska, May 4-9:34-43.
[24] Shi Y and Eberhart RC.Fuzzy Adaptive Particle Swarm Optizimation.In Proceedings of the Congress on Evolutionary Computation 2001,Seoul,Korea,IEEE Service Center,2001: 101-106.
[25] Shi Y and Eberhart RC.Comparing Inertia Weights and Constriction Factro in Particle Swarm Optizimation.In Proceedings of the IEEE International Congress on Evolutionary Computation,vol. 1:84-88.
[26] Ratnaweera A,Halgamuge SK,and Watsom HC.Self-Organizing Hierarchical ParticleSwarm Optimizer with Time-Varying Acceleration Coefficients, IEEE Transactions on Evolutionary Computation.2004,8.3:240-255.
[27] Kennedy J,R Mendes.Topological Structure and Particle Swarm Performance.The 2002 Congress on Evolutionary Computation.Piscataway.IEEE Service Center:2002:1671-1676.
[28] Mendes R,Kennedy J.The Full Informed Particle Swarm:simpler,maybe better.IEEE TRANSACTION ON EVOLUTIONARY COMPUTATION,2004,8.3:204-210.
[29] Mendes R.Population Topologies and Their Influence in Particle Swarm Performance. PHD Dissertation.Department of Information,University of Minho,Braga,Portugal.2004
[30] Brits.R.,A.P.Engelbreeht,and F. Vanden Bergh. A Niching Partiele Swarm Optimiler . In Proeeedings of the Conference on Simulated Evolution and Learning. November,2002, SingaPore.
[31]贾东立,张家树.基于混沌变异的小生境粒子群算法.控制与决策.2007,22.1:117-120.
[32]庞巍,王康平,周春光,黄岚,季晓辉.模糊离散粒子群优化算法求解旅行商问题.小型微型计算机系统.2005,26.8:1331-1334.
[33]周雅兰,王甲海,印鉴.一种基于分布估计的离散粒子群优化算法.电子学报.2008,36.6:1242-1248.
[34]叶东毅,廖建坤.最小约简问题的一个免疫离散粒子群算法.小型微型计算机系统.2008,29.6:1089-1092.
[35] SUN J,FENG B, XUW. Particle Swarm Optimization with Particle Having Quantum Behavior.IEEE Proc. of Congress on Evolutionar Computation.2004:325-331.
[36] Yang S,Wang M,Jiao L.A quantum particle swarm optimization.Proceeding of the IEEE Congress on Evolutionary Computation,2004:320-324.
[37] S.Das ectal.Particle Swarm Optimization and Differential Evolution Algorithms: Technical Analysis, Applications and Hybridization Prespectives.Studies in Computional Intelligence.2008,116:1-38.
[38] Yi-Tung Kao,Erwie Zahara.A hybrid genetic algorithm and particle swarm optimization for multimodal functions.Applied Soft Computing.2008,8:849-857.
[39]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.
[40] P.S.Shelokar,Patrick Siarry, V.K.Jayaraman, B.D.Kulkarni.Particle swarm and ant colony algorithms hybridized for improved continuous optimization.Applied Mathematics and Computation. 2007,188:129-142.
[41]高鹰,谢胜利.混沌粒子群优化算法.计算机科学.2004,31.8:13-15.
[42] Bo Liu,Hongxia Pan.A Hybrid PSO-DV Based Intelligent Method for Fault Diagnosis of Gear-box.2009 IEEE International Symposium on Computational Intelligence in Robotics and Automation.2009:236-242.
[43] Liu Bo, Pan Hongxia. Fault Diagnosis of Bearing Using Wavelet Packet Transform and PSO-DV Based neural network . The 6th International Conference on Natural Computation.2010:1127-1133.
[44] Wei Xiuye, Pan Hongxia and Ma Qingfeng. Application of Particle Swarm Optimization Based Neural Network to Fault Diagnosis.Journal of Vibration,Measurement & Diagnosis.2006,26:133-137.
[45] Liu Bo,Pan Hongxia.FAULT DIAGNOSIS OF BEARING USING PSO WITH DIFFERENTIAL OPERATOR AND NEURAL NETWORK . The 9th International Conference on Foundations and Applications of Computational Intelligence.2010:467-473.
[46] Qian-jin Guo,Hai-bin.A hybrid PSO-GD based intelligent method for machine diagnosis.Digital Signal Processing.2006,16:402-410.
[47]车林仙,何兵,易建,陈长忆,罗佑新.对称结构Stewart机构位置正解的改进粒子群算法.农业机械学报.2008,39.10:158-163.
[48]曾议,竺长安.基于群智能算法的设备布局离散优化研究.计算机集成制造系统.2007,13.3:541-552.
[49]方红庆.一种改进粒子群算法及其在水轮机控制器PID参数优化中的应用.南京理工大学学报(自然科学版).2008,32.3:274-277.
[50]赵波,曹一家.电力系统无功优化的多智能体粒子群优化算法.中国电机工程学报.22008,25.5:1-7.
[51]韩恺,赵均,钱积新.电力系统机组组合问题的闭环粒子群算法.电力系统及自动化.2009,33.1:36-69.
[52]余欣梅,李妍,熊信良,吴耀武.基于PSO考虑谐波影响的补偿电容器优化配置.中国电机工程学报.2003,23. 2:26-120
[53]熊伟丽,徐保国.基于PSO-SVR的发酵过程状态预估模型.南京理工大学学报(自然科学版).2008,32.4:517-521.
[54]孔力,程晶晶,宋胜利,苏日建.基于改进粒子群优化技术的拜耳法物料平衡计算.华中科技大学学报(自然科学版).2008,36.1:95-98.
[55]秦元庆,孙德宝,李宁,马强.基于粒子群算法的移动机器人路径规划.机器人.2004, 26.5:222-225.
[56]薛英花,田国会,李国栋.利用改进的粒子群算法的机器人全局路径规划,华中科技大学学报(自然科学版).2008,36.S1期:167-170
[57]邓高峰,张雪萍,刘彦萍.一种障碍环境下机器人路径规划的蚁群粒子群.控制理论与应用.2009,26.8:879-883.
[58]翟学敏,刘渊,刘波,毕蓉蓉.改进的XML智能数据清洗策略.计算机工程.2009,35.4:66-71.
[59]唐贤伦,庄陵,李银国,曹长修.基于粒子群优化和模糊c均值聚类的入侵检测.计算机工程.2008,34.4:13-15.
[60]张倩,段高燕,张晓光,蒋达娅.PSO算法用于WDM系统多信道PMD补偿的研究.光子学报.2008,37.9:1829-1832.
[61]龙银芳,赵知劲,赵治栋.基于离散粒子群算法的MC-CDMA多用户检测.压电与声光.2009,31.3:305-308.
[62]刘洪武,冯全源.分集接收的STBC-MC-CDMA系统中基于PSO算法的多用户检测.电子与信息学报.2009,31.1: 45-48
[63]丁英强,孙雨耕,李婷雪.基于时分频分的无线传感器分簇网络MAC层协议.天津大学学报.2008,41.8:904-910.
[64] Shi Y,Ebethart R.C.A modified Optimizer.Proceedings of the IEEE Congresson Evolutionary Computation.Piseataway,1998:69-73.
[65] Y.Shi,R.C.Eberhart.Parameter selection in particle swarm optimization,Evolutionary Programming VII.Lecture Notes in Computer Science.1998,1447:591-600.
[66] Y.Shi,R.C.Eberhart.Fuzzy adaptive particle swarm optimizer.Proceedings of the IEEE Congress on Evolutionary Computation.IEEE Service Center:2001:87-93.
[67] Kennedy J.Small Worlds and Mega-Minds:Effects of Neighborhood Topology on Particle Swarm Performance . Proceedings of the 1999 Congress of Evolutionary Computation,vol. 3. IEEE Press: New York:1931-1938.
[68] Vanden Bergh F,Engelbrecht A.A Cooperative Approach to Particle Swarm Optimization,.IEEE Transactions on Evolutionary Computation.2004,8.3:89-95.
[69] A. Ratnaweera,S.K.Halgamuge.Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficient . IEEE Transaction on Evolutionary Computation .2004,8.3:240-255.
[70] K.C.Tan , E.J.Teoh , Q.Yua,K.C.Goh. A hybrid evolutionary algorithm for attribute selection in data mining.Expert Systems with Applications. 2009,36:8616-8630.
[71]曾建潮,介倩,崔志华.微粒群算法.北京:科学出版社,2004.
[72] Kennedy J,Eberhart R.C.A Discrete Binary Version of the Particle Swarm Algorithm.The 1997 Conference on System, Cybernetics and Informatics.Piscataway: 1997:4104-4109.
[73]魏秀业.基于粒子群优化的齿轮箱智能故障诊断研究.博士学位论文.中北大学,2009,6.
[74]理查德.格里格,菲利普.津巴多(著),王垒等译.心理学与生活,北京:人民邮电出版社,2003,10.
[75]纪震,廖惠连,吴青华.粒子群算法及应用.北京:科学出版社,2009,1.
[76] Boyd R,Recharson P.Culture and the evolutionary process.Chicago:University of Chicago Press,1985.
[77] Marcin Molga , Czes?aw Smutnicki . Test functions for optimization needs.http://www.zsd.ict.pwr.wroc.pl/files/docs/functions.pdf.
[78] J.J.Liang,A.K.Qin,Ponnuthurai Nagaratnam Suganthan,S.Baskar.Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodal Functions.IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION.2006,10.3:281-295
[79] Qi Kang,Lei Wang,Qi-di Wu.A novel ecological particle swarm optimization algorithm and its population dynamics analysis.Applied Mathematics and Computation. 2008,205:61-72.
[80] Chia-Nan Ko,Ying-Pin Chang.An orthogonal-array-based particle swarm optimizerwith nonlinear time-varying evolution.Applied Mathematics and Computation.2007,191:272-279.
[81]颜玉玲.滚动轴承的振动监测及故障诊断.机械研究与应用.1998.
[82]葛哲学,孙志坚.神经网络理论与MATALAB R2007实现.北京:电子工业出版社,2007
[83] Chen DeBao,Zhao ChunXia.Particle swarm optimization with adaptive population size and its application.Applied Soft Computing.2009,9:39-48.
[84]钟秉林,黄仁.机械故障诊断学.北京:机械工业出版社,2002.
[85]鄂加强.智能故障诊断及其应用.长沙:湖南大学出版社,2006,12.
[86]于德介,程军胜等.Hilbert能量谱及其在齿轮箱故障诊断中的应用.湖南大学学报,2003,30. 4:47-50.
[87] CHENG Jun-Sheng,YU De-Jie,YANG Yu.A method for gear fault diagnosis based on empirical mode decomposition.International Journal of Plant Engineering and Management,2004,9.4:230-235.
[88]栾军英,康海英等.基于阶次跟踪和BP网络的齿轮箱故障模式识别.军械工程学院学报.2006,18.2:20-23.
[89]田昊,栾军英等.阶次跟踪分析在齿面磨损故障诊断中的应用[J].军械工程学院学报,2005,17(5):57-60.
[90]盛兆顺,尹琦岭.设备状态监测与故障诊断技术及应用.北京:化学工业出版社.2003,6.
[81]杨军,冯振声,黄考利.装备智能故障诊断技术.北京:国防工业出版社.2004.8
[92]潘宏侠,姚竹亭.齿轮传动系统状态检测与故障诊断.华北工学院学报,2001,22.4:313-318.
[93]丁康.传动箱齿轮与轴故障的振动方法研究.冲击与振动.1994,13.2:26-32.
[94]韩捷.齿轮故障的振动频谱机理研究.机械传动.1997,12:34-37.
[95] JIA Rong,XU Qi-hui,LI Hui ,LIU Wei.Power Transformer Fault Diagnosis via Neural Network Based on Particle Swarm Optimization with Neighborhood Operator.High Voltage Apparatus.2008,44:8-19.
[96] DING Jing-guo1,JIAO Jing-min1,ZANPei. Hot Strip Width Prediction During Rough Rolling with PSO-Neural Network Based on Fuzzy Clustering.Journal of Northeastern University(NaturalScience).2007,28:1282-1292.
[97] Jialin Zhou.PSO-based neural network optimization and its utilization in a boring machine.Journal of Materials Processing Technology.2006,178:19-23.
[98] Yi Daa,Ge Xiurun.An improved PSO-based ANN with simulated annealing technique.Neurocomputing.2005,63 .10:527-533
[99] Qian-jin Guo,Hai-bin.A hybrid PSO-GD based intelligent method for machine diagnosis.Digital Signal Processing.2006,16:402-411.
[100]潘旭峰等.齿轮轴承故障诊断中特征参数敏感性分析.兵工学报.1997,18.4: 294-297.
[101] Jian-Da Wu,Chuang-Chin Hsu.Fault gear identification using vibration signal with discrete wavelet transform technique and fuzzy-logic inference.Expert Systems with Applications.2009,36:3785-3794.
[102]胡寿松.自动控制原理(第四版).北京:科学出版社,2001,2
[103] Dong Hwa Kim.GA–PSO based vector control of indirect three phase induction motor.Applied Soft Computing. 2007,7:601-611.
[104] U.Zuperl,F.Cus, M.Reibenschuh.Neural control strategy of constant cutting force system in end milling.Robotics and Computer-Integrated Manufacturing.2011,27:485-493
[105] Hongqing Fang,Long Chen,Zuyi Shen.Application of an improved PSO algorithm to optimal tuning of PID gains for water turbine governor.Energy Conversion and Management2011,52:1763-1770.
[106] Majid Zamani,Masoud Karimi-Ghartemani,Nasser Sadati,Mostafa Parniani.Design of a fractional order PID controller for an AVR using particle swarm optimization.Control Engineering Practice.2009,17:1380-1387.
[107]谈乐斌,张相炎,管红根,等.火炮概论.北京:北京理工大学出版社,2005.
[108]杨惠勇,张培林,阎鹏程,等.火炮身管弯曲度检测方法研究.兵工自动化,2008,27.9:19-21.
[109]赵剑,谢宗蕻,张磊.高温合金热防护系统设计与分析.宇航学报,2008,29.5:28-32.
[110]罗来科,宣益民,韩玉阁.坦克炮管温度场的有限元计算.兵工学报,2005,26.1:6-9.
[111] Yi-Xiong Jin,Hao-Zhong Cheng,Jian-yong Yan,Li Zhang.New discrete method for particle swarm optimization and its application in transmission network expansion planning. Electric Power Systems Research.2007,77:227-233.
[112] Ching-Jong Liao,Chao-Tang Tseng,Pin Luarn.A discrete version of particle swarm optimization for flowshop scheduling problems.Computers & Operations Research,2007,34:3099-3111.
[113] Bin Jiao,Zhigang Lian,Xingsheng Gu.A dynamic inertia weight particle swarm optimization algorithm.Chaos, Solitons and Fractals.2008,37:698-705.
[114]君兰工作室.机电一体化-从原理到应用.北京,科学出版社:2009
[115] Hongcheng Liu,Liang Gao,Quanke Pan.A hybrid particle swarm optimization with estimation of distribution algorithm for solving permutation flowshop scheduling problem. Expert Systems with Applications.2010,43:9645-9653.
[116] M. Fatih Tasgetiren,Yun-Chia Liang,Mehmet Sevkli,Gunes Gencyilmaz.A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem.European Journal of Operational Research.2007,177:1930-1947.
[117] B . Naderia , RubénRuizb . The distributed permutation flowshop schedulingproblem.Computers & Operations Research.2010,37 :754-768.
[118] Zhigang Lian,Xingsheng Gu,Bin Jiao.A similar particle swarm optimization algorithm for permutation flowshop scheduling to minimize makespan.Applied Mathematics and Computation.2006,175:773-785.
[119] Bo Liu,Ling Wang,Yi-Hui Jin.An Effective PSO-Based Memetic Algorithm for Flow Shop Scheduling . IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS.2007,37:18-27.
[120] Bean J C.Genetic algorithm and random keys for sequencing and optimization.ORSA Journal of Computing.1994,6.2:154-160.
[121]李志耕,黄佩君,赵以裕,马长瀛.GB50171-1992,1992.电气装置安装工程盘、柜及二次回路结线施工及验收规范.
[122]中国工程建设标准化协会.CECS 49-1993,1993.低压成套开关设备验收规范.
[123] Nawaz M,Enscore E,Ham I.A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem.Omega.1983,11:91-95.