Indirect adaptive self-organizing RBF neural controller design with a dynamical training approach

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Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/107543

Title:	Indirect adaptive self-organizing RBF neural controller design with a dynamical training approach
Authors:	蔡章仁;Hsu, Chun-Fei;Chiu, Chien-Jung;Tsai, Jang-Zern
Contributors:	資訊電機學院電機工程學系
Keywords:	Adaptive control;Adaptive control systems;Compensators;Control systems;Dynamical learning rate;Dynamical systems;Dynamics;Learning;Networks;Neural control;On-line systems;RBF network;Self-organizing
Date:	2012-01-01
Issue Date:	2026-04-23 14:16:52 (UTC+8)
Publisher:	Elsevier Ltd.;Elsevier Ltd
Abstract:	摘要： ► The self-organizing RBF network can add and prune hidden neurons online. ► The self-organizing RBF network can achieve better learning accuracy than others. ► The proposed control scheme can effectively control two chaotic systems. ► The dynamical learning rate can speed up the convergence of the tracking error. This study proposes an indirect adaptive self-organizing RBF neural control (IASRNC) system which is composed of a feedback controller, a neural identifier and a smooth compensator. The neural identifier which contains a self-organizing RBF (SORBF) network with structure and parameter learning is designed to online estimate a system dynamics using the gradient descent method. The SORBF network can add new hidden neurons and prune insignificant hidden neurons online. The smooth compensator is designed to dispel the effect of minimum approximation error introduced by the neural identifier in the Lyapunov stability theorem. In general, how to determine the learning rate of parameter adaptation laws usually requires some trial-and-error tuning procedures. This paper proposes a dynamical learning rate approach based on a discrete-type Lyapunov function to speed up the convergence of tracking error. Finally, the proposed IASRNC system is applied to control two chaotic systems. Simulation results verify that the proposed IASRNC scheme can achieve a favorable tracking performance. 出版者： Elsevier Ltd 出版日期： 2012 出處： Expert systems with applications, 2012, Vol.39 (1), p.564-573 版權： 2011 Elsevier Ltd 識別號： ISSN: 0957-4174 識別號： EISSN: 1873-6793 識別號： DOI: 10.1016/j.eswa.2011.07.047
Appears in Collections:	[Department of Electrical Engineering] journal & Dissertation

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