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Title: Genetic algorithm-based variable translation wavelet neural network and its application
Authors: Ling, SH
Leung, FHF 
Issue Date: 2005
Source: 2005 IEEE International Joint Conference on Neural Networks (IJCNN) : Montreal, QC, Canada, July 31-August 4, 2005, p. 1365-1370
Abstract: A variable translation wavelet neural network (VTWNN) trained by genetic algorithm is presented in this paper. In the proposed wavelet neural network, the translation parameters are variables depending on the network inputs. Thanks to the variable translation parameter, the network becomes an adaptive one, providing better performance and increased learning ability than conventional wavelet neural networks. Genetic algorithm is applied to train the parameters of the proposed wavelet neural network. An application example on short-term daily electric load forecasting in Hong Kong is presented to show the merits of the proposed network.
Keywords: Electric load forecasting
Genetic algorithms
Parameter estimation
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.
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