Please use this identifier to cite or link to this item: 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 
Keywords: Database systems
Genetic algorithms
Parameter estimation
Pattern recognition
Issue Date: 2005
Publisher: IEEE
Source: 2005 IEEE International Joint Conference on Neural Networks (IJCNN) : Montreal, QC, Canada, July 31-August 4, 2005, p. 1343-1348 How to cite?
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.
URI: http://hdl.handle.net/10397/1396
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.
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