Transiently chaotic neural networks with piecewise linear output functions

Shyan-Shiou Chen and Chih-Wen Shih

Chaos, Solitons & Fractals, 2009, vol. 39, issue 2, 717-730

Abstract: Admitting both transient chaotic phase and convergent phase, the transiently chaotic neural network (TCNN) provides superior performance than the classical networks in solving combinatorial optimization problems. We derive concrete parameter conditions for these two essential dynamic phases of the TCNN with piecewise linear output function. The confirmation for chaotic dynamics of the system results from a successful application of the Marotto theorem which was recently clarified. Numerical simulation on applying the TCNN with piecewise linear output function is carried out to find the optimal solution of a travelling salesman problem. It is demonstrated that the performance is even better than the previous TCNN model with logistic output function.

Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:39:y:2009:i:2:p:717-730

DOI: 10.1016/j.chaos.2007.01.103

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