Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1395
Title: Stability analysis, synthesis and optimization of radial-basis-function neural-network based controller for nonlinear systems
Authors: Lam, HK
Leung, FHF 
Keywords: Control equipment
Control systems
Genetic algorithms
Matrix algebra
Nonlinear systems
Optimization
Pendulums
Issue Date: 2004
Publisher: IEEE
Source: IECON 2004 : 30th annual conference of IEEE Industrial Electronics Society : Busan, South Korea, 2-6 November 2004, p. 2813-2818 How to cite?
Abstract: This paper presents the stability analysis, synthesis, and performance optimization of a radial-basis-function neural-network based control system. Global stability conditions will be derived in terms of matrix measure. Based on the derived stability conditions, connection weights of the radial-basis-function neural-network based controller can be optimized by genetic algorithm (GA) subject to the system stability. Furthermore, the system performance will also be optimized by the GA. An application example on stabilizing an inverted pendulum will be given to illustrate the design procedure and merits of the proposed approach.
URI: http://hdl.handle.net/10397/1395
ISBN: 0-7803-8730-9
Rights: © 2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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