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Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/1361
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| Title: | Gain estimation for an AC power line data network transmitter using a self-structured neural network and genetic algorithm |
| Authors: | Lam, H. K.
Ling, S. H.
Leung, Frank H. F.
Tam, Peter K. S.
Lee, Yim-shu |
| Subjects: | Distributed parameter control systems
Gain control
Genetic algorithms
Neural networks
Power control
SCADA systems |
| Issue Date: | 2002 |
| Publisher: | IEEE |
| Citation: | IECON-2002 : Sevilla, Spain, November 5-8, 2002 : proceedings of the 2002 28th Annual Conference of the IEEE Industrial Electronics Society, p. 1926-1929. |
| Abstract: | This paper presents the estimation of the transmission gain for the AC power line data network in an intelligent home. The estimated gain ensures the transmission reliability and efficiency. A neural network with link switches is proposed to perform the estimation. Genetic algorithm with arithmetic crossover and non-uniform mutation is employed to tune the parameters and the structure of the proposed neural network. An application example will be given. |
| Rights: | © 2002 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. |
| Type: | Conference Paper |
| URI: | http://hdl.handle.net/10397/1361 |
| ISBN: | 0-7803-7474-6 |
| Appears in Collections: | EIE Conference Papers & Presentations
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