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, H. K.
Leung, Frank H. F.
Subjects: 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.
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.
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Type: Conference Paper
URI: http://hdl.handle.net/10397/1395
ISBN: 0-7803-8730-9
Appears in Collections:EIE Conference Papers & Presentations

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