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.
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Huang, M., Cui, B. (2005). A Novel Learning Algorithm for Wavelet Neural Networks. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_1
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DOI: https://doi.org/10.1007/11539087_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28323-2
Online ISBN: 978-3-540-31853-8
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