2010Unpublished venueRequires access

Activation Function of Wavelet Chaotic Neural Networks

Jiahai Zhang, Haixia Chen

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Abstract

Chaotic neural networks have been proved to be powerful tools for escaping from local minima. In this paper, we first review Chen's chaotic neural network and then propose a novel chaotic neural network model. Second, we make an analysis of the most positive Lyapunov exponent of the neural units of Chen's and the proposed 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 we proposed is more valid.

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What this paper is about

Chaotic neural networks have been proved to be powerful tools for escaping from local minima. In this paper, we first review Chen's chaotic neural network and then propose a novel chaotic neural network model. Second, we make an analysis of the most positive Lyapunov exponent of the neural units of Chen's and the proposed 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 we proposed is more valid.

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Available abstract

Chaotic neural networks have been proved to be powerful tools for escaping from local minima. In this paper, we first review Chen's chaotic neural network and then propose a novel chaotic neural network model. Second, we make an analysis of the most positive Lyapunov exponent of the neural units of Chen's and the proposed 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 we proposed is more valid.

Key concepts: Chaotic, Maxima and minima, Artificial neural network, Chen, Lyapunov exponent, Computer science, Travelling salesman problem, Activation function

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