Wavelet Neural Network Optimization Based on Hybrid Hierarchy Genetic Algorithm

Xiao Bo Yang; Ji Ning Feng; Zhe Jun Diao; Hong Yun Liu

doi:10.4028/www.scientific.net/AMR.433-440.823

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 433-440Wavelet Neural Network Optimization Based on...

Wavelet Neural Network Optimization Based on Hybrid Hierarchy Genetic Algorithm

Abstract:

Based on studying wavelet neural network (WNN) training algorithm and geometrical structure, a new WNN optimization algorithm-hybrid hierarchy genetic is introduced. The algorithm is combined by hierarchy genetic algorithm and linear multi-regress. Hybrid hierarchy genetic algorithm (HHGA) can determine the structure and parameters of WNN from data at one time. The method has the merits of faster learning speed, higher precision. It is compared with traditional BP algorithm in this paper. The effectiveness of the algorithm is demonstrated.

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Periodical:

Advanced Materials Research (Volumes 433-440)

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823-828

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https://doi.org/10.4028/www.scientific.net/AMR.433-440.823

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Online since:

January 2012

Authors:

Xiao Bo Yang, Ji Ning Feng, Zhe Jun Diao, Hong Yun Liu

Keywords:

Hybrid Hierarchy Genetic Algorithm, Optimization, Wavelet Neural Network (WNN)

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