Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1418
Title: A variable node-to-node-link neural network and its application to hand-written recognition
Authors: Ling, SH
Leung, FHF 
Lam, HK
Keywords: Codes (symbols)
Genetic algorithms
Parameter extraction
Pattern recognition
Real time systems
Issue Date: 2006
Publisher: IEEE
Source: IJCNN '06 : 2006 International Joint Conference on Neural Networks : Vancouver, BC, Canada, July 16-21, 2006, p. 921-928 How to cite?
Abstract: This paper presents a variable node-to-node-link neural network (VN²NN) trained by real-coded genetic algorithm (RCGA). The VN²NN exhibits a node-to-node relationship in the hidden layer, and the network parameters are variable. These characteristics make the network adapt to the changes of the input environment, enable it to tackle different input sets distributed in a large domain. Each input data set is effectively handled by a corresponding set of network parameters. The set of parameters are governed by the other nodes. Taking the advantage of these features, the proposed network ensures better learning and generalization abilities. Application of the proposed network to hand-written graffiti recognition will be presented so as to illustrate the improvement.
URI: http://hdl.handle.net/10397/1418
ISBN: 0-7803-9490-9
Rights: © 2006 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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