Improved transiently chaotic neural network and its application to optimization

Authors:
Yao-qun Xu;Ming Sun;Meng-shu Guo
Affiliations:
Institute of System Engineering, Harbin University of Commerce, Harbin, China;Institute of System Engineering, Harbin University of Commerce, Harbin, China;Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin, China
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Year:
2006

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Abstract

A wavelet function was introduced into the activation function of the transiently chaotic neural network in order to solve combinational optimization problems more efficiently. The dynamic behaviors of chaotic signal neural units were analyzed and the time evolution figures of the maximal Lyapunov exponents and chaotic dynamic behavior were given. The improved transiently chaotic neural network has the ability to stay in chaotic states longer because the wavelet function is non-monotonous and is a kind of basic function. The simulation results prove that the improved transiently chaotic neural network is superior to the original in solving 10-city traveling salesman problem (TSP).