A study of the transiently chaotic neural network for combinatorial optimization

Authors:
Zhen Ding;Henry Leung;Zhiwen Zhu
Affiliations:
New Ventures Group, Raytheon Canada Limited 400 Philip St., Waterloo, Ontario, N2J 4K6 Canada;Department of Electrical and Computer Engineering, University of Calgary 2500 University Drive N.W., Calgary, Alberta, T2N 1N4 Canada;Toronto Multimedia Application Center Nortel Networks, Toronto, Ontario, M5G 1W7 Canada
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
2002

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Abstract

We consider the dynamic behavior of the transiently chaotic neural network (TCNN). Although its dynamical behavior is of interest in investigating neural dynamics, we observed that the chaotic phase in a TCNN is not a necessary condition for the network to reach the global solution for a combinatorial optimization problem. In fact, the global solution is a result of the time varying terms in a TCNN. We, therefore, generalized the TCNN to a nonautonomous Hopfield neural network (NAHNN). Simulation shows that the NAHNN is very effective in solving the traveling salesman problem (TSP).