Non-Gaussian System Identification Based on Improved Estimation of Distribution Algorithm
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- 英文论文题名：Non-Gaussian System Identification Based on Improved Estimation of Distribution Algorithm
- 论文作者：Jinglin Zhou ; Yiqing Jia ; Han Zhang ; Jing Wang ; Haijiang Zhu
- 英文论文作者：Jinglin Zhou ; Yiqing Jia ; Han Zhang ; Jing Wang ; Haijiang Zhu ; College of Information Science &Technology ; Beijing University of Chemical Technology
- 年：2017
- 作者机构：College of Information Science &Technology,Beijing University of Chemical Technology;
- 英文论文关键词：Non-Gaussian System ; EDA ; Parameter Estimation
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（B）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
- 语种：英文
- 分类号：O231
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：6
- 文件大小：633k
- 原文格式：D
- 会议级别：全国
摘要
In this paper, an improved estimation of distribution algorithm(EDA) is proposed and applied to the identification of ARMA model parameters. The system parameter identification problem is transformed into the optimization problem in high dimensional parameter space. Based on the traditional EDA algorithm, the parameters of preliminary estimation and data selection are added to improve the speed of searching and optimization precision. Because the mean and the variance are not enough to describe the uncertainty of non-Gaussian system, the entropy is regarded as fitness value to achieve the parameter identification of non-Gaussian system. Finally, an example of improved EDA identification is given to illustrate the effectiveness of the proposed approach.
In this paper, an improved estimation of distribution algorithm(EDA) is proposed and applied to the identification of ARMA model parameters. The system parameter identification problem is transformed into the optimization problem in high dimensional parameter space. Based on the traditional EDA algorithm, the parameters of preliminary estimation and data selection are added to improve the speed of searching and optimization precision. Because the mean and the variance are not enough to describe the uncertainty of non-Gaussian system, the entropy is regarded as fitness value to achieve the parameter identification of non-Gaussian system. Finally, an example of improved EDA identification is given to illustrate the effectiveness of the proposed approach.
In this paper, an improved estimation of distribution algorithm(EDA) is proposed and applied to the identification of ARMA model parameters. The system parameter identification problem is transformed into the optimization problem in high dimensional parameter space. Based on the traditional EDA algorithm, the parameters of preliminary estimation and data selection are added to improve the speed of searching and optimization precision. Because the mean and the variance are not enough to describe the uncertainty of non-Gaussian system, the entropy is regarded as fitness value to achieve the parameter identification of non-Gaussian system. Finally, an example of improved EDA identification is given to illustrate the effectiveness of the proposed approach.
引文
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[2]X.B.Feng and K.A.Loparo,Active probing for information in control system with quantized state measurements:A minimum entropy approach,IEEE Trans.Automat.Control,vol.42,no.2,pp.216–238,Feb.1997.
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[6]Goldberg D E.Genetic Algorithms in Search,Optimization and Machine Learning.1989,xiii.
[7]Kristinsson K,Dumont G A.System identification and control using genetic algorithms.IEEE Transactions on Systems Man&Cybernetics,1992,22(5):1033-1046.
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[9]Ye G,Li W,Wan H.Study of RBF Neural Network Based on PSO Algorithm in Nonlinear System Identification International Conference on Intelligent Computation Technology and Automation IEEE,2015:852-855.
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[12]Qin A K,Huang V L,Suganthan P N.Differential evolution algorithm with strategy adaptation for global numerical optimization.IEEE Transactions on Evolutionary Computation,2009,13(2):398-417.
[13]Hauschild M,Pelikan M.An introduction and survey of estimation of distribution algorithms.Swarm and Evolutionary Computation,2011,1(3):111-128.
[14]Larranaga P,Lozano J A.Estimation of distribution algorithms:A new tool for evolutionary computation.Boston:Kluwer Press,2002.
[15]Tjalling J.Ypma,Historical development of the Newton-Raphson method,Society for Industrial and Applied Mathematics,1995.
[2]X.B.Feng and K.A.Loparo,Active probing for information in control system with quantized state measurements:A minimum entropy approach,IEEE Trans.Automat.Control,vol.42,no.2,pp.216–238,Feb.1997.
[3]A.Papoulis,Probability,Random Variables and Stochastic Processes,3rd ed.New York:McG raw-Hill,1991.
[4]A.Renyi,A Diary on Information Theory.New York:Wiley,1987.
[5]L.Guo and H.Wang,Minimum entropy filtering for multivariate stochastic systems with non-gaussian noises,IEEE Trans.Automat.Control,vol.51,no.4,pp.695–700,Apr.2006.
[6]Goldberg D E.Genetic Algorithms in Search,Optimization and Machine Learning.1989,xiii.
[7]Kristinsson K,Dumont G A.System identification and control using genetic algorithms.IEEE Transactions on Systems Man&Cybernetics,1992,22(5):1033-1046.
[8]Lu Y,Sundararajan N,Saratchandran P.Performance evaluation of a sequential minimal radial basis function(RBF)neural network learning algorithm.IEEE Transactions on Neural Networks,1998,9(2):308-318.
[9]Ye G,Li W,Wan H.Study of RBF Neural Network Based on PSO Algorithm in Nonlinear System Identification International Conference on Intelligent Computation Technology and Automation IEEE,2015:852-855.
[10]Eberhart R C,Shi Y.Particle swarm optimization:Development,applications and resources.Evolutionary Computation,Proceedings of the 2001 Congress on.2001:81-86 vol.1.
[11]Trelea I C.The particle swarm optimization algorithm:convergence analysis and parameter selection.Information Processing Letters,2003,85(6):317-325.
[12]Qin A K,Huang V L,Suganthan P N.Differential evolution algorithm with strategy adaptation for global numerical optimization.IEEE Transactions on Evolutionary Computation,2009,13(2):398-417.
[13]Hauschild M,Pelikan M.An introduction and survey of estimation of distribution algorithms.Swarm and Evolutionary Computation,2011,1(3):111-128.
[14]Larranaga P,Lozano J A.Estimation of distribution algorithms:A new tool for evolutionary computation.Boston:Kluwer Press,2002.
[15]Tjalling J.Ypma,Historical development of the Newton-Raphson method,Society for Industrial and Applied Mathematics,1995.