Abstract
Chaotic neural networks have been proved to be powerful tools for escaping from local minima. In this paper, we first retrospect Chen’s chaotic neural network and then propose a novel Gauss-Morlet-Sigmoid chaotic neural network model. Second, we make an analysis of the largest Lyapunov exponents of the neural units of Chen’s and the Gauss-Morlet-Sigmoid model. Third, 10-city traveling salesman problem (TSP) is given to make a comparison between them. Finally we conclude that the novel chaotic neural network model is more effective.
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Xu, Yq., Sun, M. (2006). Gauss-Morlet-Sigmoid Chaotic Neural Networks. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_12
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DOI: https://doi.org/10.1007/11816157_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37271-4
Online ISBN: 978-3-540-37273-8
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