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Title: Genetic algorithm based variable-structure neural network and its industrial application
Authors: Ling, SH
Leung, FHF 
Lam, HK
Issue Date: 2004
Source: IECON 2004 : 30th annual conference of IEEE Industrial Electronics Society : Busan, South Korea, 2-6 November 2004, p. 1273-1278
Abstract: This paper presents a neural network model with a variable structure, which is trained by an improved genetic algorithm (GA). The proposed variable-structure neural network (VSNN) consists of a Neural Network with Link Switches (NNLS) and a Network Switch Controller (NSC). In the NNLS, switches in its links between the hidden and output layers are introduced. By introducing the NSC to control the switches in the NNLS, the proposed neural network can model different input patterns with variable network structures. The proposed network gives better results and increased learning ability than conventional feed-forward neural networks. An industrial application on short-term load forecasting in Hong Kong is given to illustrate the merits of the proposed network.
Keywords: Electric connectors
Electric control equipment
Electric load forecasting
Genetic algorithms
Industrial applications
Pattern recognition
Switching networks
Publisher: IEEE
ISBN: 0-7803-8730-9
Rights: © 2004 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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