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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 |
Issue Date: | 2004 | 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. | Keywords: | Control equipment Control systems Genetic algorithms Matrix algebra Nonlinear systems Optimization Pendulums |
Publisher: | IEEE | 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. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. |
| Appears in Collections: | Conference Paper |
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