Abstract
Chaotic neural networks have been proved to be strong tools to solve the optimization problems. In order to escape the local minima, a new chaotic neural network model called Shannon wavelet chaotic neural network was presented. The activation function of the new model is non-monotonous, which is composed of sigmoid and Shannon wavelet. First, the figures of the reversed bifurcation and the maximal Lyapunov exponents of single neural unit were given. Second, the new model is applied to solve several function optimizations. Finally, 10-city traveling salesman problem is given and the effects of the non-monotonous degree in the model on solving 10-city traveling salesman problem are discussed. The new model can solve the optimization problems more effectively because of the Shannon wavelet being a kind of basic function. Seen from the simulation results, the new model is powerful.
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Xu, Yq., Sun, M., Shen, Jh. (2006). Shannon Wavelet Chaotic Neural Networks. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_32
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DOI: https://doi.org/10.1007/11903697_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47331-2
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