Intelligent control of chaotic systems via self-organizing Hermite-polynomial-based neural network

doi:10.1016/j.neucom.2013.07.008

淡江大學機構典藏 > 工學院 > 電機工程學系暨研究所 > 期刊論文 > Item 987654321/93537

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题名:	Intelligent control of chaotic systems via self-organizing Hermite-polynomial-based neural network
作者:	Hsu, Chun-Fei
贡献者:	淡江大學電機工程學系
关键词:	Adaptive control;Neural control;Hermite polynomial function;Self-organizing;Lyapunov stability;Chaotic system
日期:	2014-01-10
上传时间:	2014-01-22 15:29:05 (UTC+8)
出版者:	Amsterdam:Elsevier BV
摘要:	This paper proposes an adaptive self-organizing Hermite-polynomial-based neural control (ASHNC) system which is composed of a neural controller and a supervisor compensator. The neural controller uses a self-organizing Hermite-polynomial-based neural network (SHNN) to approximate an ideal feedback controller. For the SHNN, the developed self-organizing approach is clearly and easily used for real-time systems and the parameter learning ability is effective with high convergence precision and fast convergence time. The supervisor compensator is designed to eliminate the approximation error between the neural controller and ideal feedback controller without chattering phenomena. Moreover, a proportional–integral (PI) type adaptation law is derived based on the Lyapunov stability theory; thus not only the system stability of the control system can be guaranteed but also the convergence of the tracking error can be speeded up. Finally, the proposed ASHNC system is applied to a chaotic system. Simulation results demonstrate that the proposed ASHNC system can achieve favorable control performance.
關聯:	Neurocomputing 123, p.197–206
DOI:	10.1016/j.neucom.2013.07.008
显示于类别:	[電機工程學系暨研究所] 期刊論文

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