Abstract
Chaotic neural networks have been proved to be powerful tools to solve the optimization problems. In order to escape the local minima, a new chaotic neural network model called Gauss Wavelet chaotic neural network was presented, and the chaotic mechanism is introduced by the attenuation of the self-feedback connection weight. The activation function of the new model is non-monotonous, which is composed of sigmoid function and Gauss Wavelet function. First, the figures of the reversed bifurcation and the maximal Lyapunov exponents of single neural unit were given. Second, the new model was applied to solve function optimizations. Finally, 10-city traveling salesman problem was given and the effects of the non-monotonous degree in the model on solving 10-city traveling salesman problem were discussed. The new model can solve the optimization problems more effectively because Gauss wavelet is a kind of basic function. Seen from the simulation results, the new model is powerful than the common chaotic neural network.
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Xu, Yq., Sun, M., Shen, Jh. (2006). Gauss Wavelet Chaotic Neural Networks. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028_52
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DOI: https://doi.org/10.1007/11893028_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46479-2
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