Asymptotic behaviors in a transiently chaotic neural network

Shyan Shiou Chen*, Chih-Wen Shih

*Corresponding author for this work

Research output: Contribution to journalArticle

8 Scopus citations


We are interested in the asymptotic behaviors of a discrete-time neural network. This network admits transiently chaotic behaviors which provide global searching ability in solving combinatorial optimization problems. As the system evolves, the variables corresponding to temperature in the annealing process decrease, and the chaotic behaviors vanish. We shall find sufficient conditions under which evolutions for the system converge to a fixed point of the system. Attracting sets and uniqueness of fixed point for the system are also addressed. Moreover, we extend the theory to the neural networks with cycle-symmetric coupling weights and other output functions. An application of this annealing process in solving travelling salesman problems is illustrated.

Original languageEnglish
Pages (from-to)805-826
Number of pages22
JournalDiscrete and Continuous Dynamical Systems
Issue number3
StatePublished - 1 Apr 2004


  • Convergence of dynamics
  • Lyapunov function
  • Neural network

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