statistical information based single neuron adaptive control for non-gaussian stochastic systems基于统计信息的非高斯随机系统的单神经元自适应控制.pdfVIP

Entropy 2012, 14, 1154-1164; doi:10.3390/ OPEN ACCESS entropy ISSN 1099-4300 /journal/entropy Article Statistical Information Based Single Neuron Adaptive Control for Non-Gaussian Stochastic Systems Mifeng Ren 1, Jianhua Zhang 2,*, Man Jiang 1, Ye Tian 1 and Guolian Hou 1 1 School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China; E-Mails: renmifeng@126.com (M.R.); jiangman410@126.com (M.J.); tianye1019@126.com (Y.T.); hgl@ (G.H.) 2 State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China * Author to whom correspondence should be addressed; E-Mail: zjh@; Tel.: +86-10-6177-2106. Received: 3 May 2012; in revised form: 22 June 2012 / Accepted: 27 June 2012 / Published: 2 July 2012 Abstract: Based on information theory, the single neuron adaptive control problem for stochastic systems with non-Gaussian noises is investigated in this paper. Here, the statistic information of the output within a receding window rather than the output value is used for the tracking problem. Firstly, the single neuron controller structure, which has the ability of self-learning and self-adaptation, is established. Then, an improved performance criterion is given to train the weights of the single neuron. Furthermore, the mean-square convergent condition of the proposed control algorithm is formulated. Finally, comparative simulation results are presented to show that the