Critical temperature of the transiently chaotic neural network

Authors:
Zhen Ding;Henry Leung;Zhiwen Zhu
Affiliations:
New Ventures Group, Raytheon Canada Limited 400 Philip St., Waterloo, ON, N2J 4K6 Canada;Department of Electrical and Computer Engineering, University of Calgary 2500 University Drive N.W., Calgary, AB, T2N 1N4 Canada;Toronto Multimedia Application Center, Nortel Networks Toronto, ON, M5G 1W7 Canada
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
2003

Citing 3
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Abstract

The dynamical behaviour of an optimizing neural network is closely related to its parameters. For the transiently chaotic neural network (TCNN), the temperature, i.e., self-feedback weighting, is an important parameter for the network performance. While a high temperature is required to investigate chaotic dynamics, a low temperature is preferred for combinatorial optimization application. In this article, we derived this critical temperature of the TCNN analytically and illustrated its validity using computer simulation.