A novel learning algorithm for wavelet neural networks

Authors:
Min Huang;Baotong Cui
Affiliations:
Control Science and Engineering Research Center, Southern Yangtze University, Wuxi, Jiangsu, P.R.China;Control Science and Engineering Research Center, Southern Yangtze University, Wuxi, Jiangsu, P.R.China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Year:
2005

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Abstract

Wavelet neural networks(WNN) are a class of neural networks consisting of wavelets. A novel learning method based on immune genetic algorithm(IGA) for continuous wavelet neural networks is presented in this paper. Through adopting multi-encoding, this algorithm can optimize the structure and the parameters of WNN in the same training process. Simulation results show that WNN with novel algorithm has a comparatively simple structure and enhance the probability for global optimization. The study also indicates that the proposed method has the potential to solve a wide range of neural network construction and training problems in a systematic and robust way.