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http://hdl.handle.net/10397/1396
| Title: | A variable-parameter neural network trained by improved genetic algorithm and its application | Authors: | Ling, SH Lam, HK Leung, FHF |
Issue Date: | 2005 | Source: | 2005 IEEE International Joint Conference on Neural Networks (IJCNN) : Montreal, QC, Canada, July 31-August 4, 2005, p. 1343-1348 | Abstract: | This paper presents a neural network with variable parameters. These variable parameters adapt to the changes of the input environment, and tackle different input data sets in a large domain. Each input data set is effectively handled by its corresponding set of network parameters. Thus, the proposed neural network exhibits a better learning and generalization ability than a traditional one. An improved genetic algorithm [1] is proposed to train the network parameters. An application example on hand-written pattern recognition will be presented to verify and illustrate the improvement. | Keywords: | Database systems Genetic algorithms Parameter estimation Pattern recognition |
Publisher: | IEEE | ISBN: | 0-7803-9048-2 | Rights: | © 2005 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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| Variable-parameter neural network_05.pdf | 965.46 kB | Adobe PDF | View/Open |
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