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Title: A genetic algorithm based variable structure neural network
Authors: Ling, SH
Lam, HK
Leung, FHF 
Lee, YS
Issue Date: 2003
Source: IECON'03 : the 29th annual conference of the IEEE Industrial Electronics Society : Roanoke, Virginia, USA, November 2nd (Sunday) to Thursday, November 6th (Thursday) 2003, p. 436-441
Abstract: This paper presents a neural network model with a variable structure, which is trained by genetic algorithm (GA). The proposed neural network consists of a Neural Network with a Node-to-Node Relationship (N[sup 4]R) and a Network Switch Controller (NSC). In the N[sup 4]R, a modified neuron model with two activation functions in the hidden layer, and switches in its links are introduced. The NSC controls the switches in the N[sup 4]R. The proposed neural network can model different input patterns with variable network structures. The proposed neural network provides better result and learning ability than traditional feed forward neural networks. Two application examples on XOR problem and hand-written pattern recognition are given to illustrate the merits of the proposed network.
Keywords: Genetic algorithm
Neural network
Hand-written pattern recognition
Publisher: IEEE
ISBN: 0-7803-7906-3
Rights: © 2003 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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