Optimal Design of Adaptive Wavelet Neural Network Based on Hierarchy Genetic Algorithm

Xu Sheng Gan; Xue Qin Tang; Jian Guo Gao

doi:10.4028/www.scientific.net/AMM.339.301

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 339Optimal Design of Adaptive Wavelet Neural Network...

Optimal Design of Adaptive Wavelet Neural Network Based on Hierarchy Genetic Algorithm

Abstract:

Based on the study of adaptive wavelet neural networks, a hierarchy genetic algorithm is proposed to training network. Compared with standard genetic algorithm, the method can not only optimize network parameters such as scale factor, transform factor parameter and connection weights, but also solve structure problem of adaptive wavelet neural network. The result of simulation indicates that the algorithm can efficiently determinate the parameter and structure of adaptive wavelet neural network, and has better higher training efficiency and forecasting precision.

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Periodical:

Applied Mechanics and Materials (Volume 339)

Pages:

301-306

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.339.301

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Online since:

July 2013

Authors:

Xu Sheng Gan, Xue Qin Tang, Jian Guo Gao

Keywords:

Fitness Function, Hierarchy Genetic Algorithm, Learning Rate, Wavelet Neural Network (WNN)

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