標題: Transiently chaotic neural networks with piecewise linear output functions
作者: Chen, Shyan-Shiou
Shih, Chih-Wen
應用數學系
Department of Applied Mathematics
公開日期: 30-一月-2009
摘要: 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 front 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 it travelling salesman problem. It is demonstrated that the performance is even better than the previous TCNN model with logistic output function. (C) 2007 Elsevier Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/j.chaos.2007.01.103
http://hdl.handle.net/11536/7718
ISSN: 0960-0779
DOI: 10.1016/j.chaos.2007.01.103
期刊: CHAOS SOLITONS & FRACTALS
Volume: 39
Issue: 2
起始頁: 717
結束頁: 730
顯示於類別:期刊論文


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  1. 000265368800024.pdf