Delayed transiently chaotic neural networks and their application

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

In this paper, we propose a novel model, a delayed transiently chaotic neural network (DTCNN), and numerically confirm that the model performs better in finding the global minimum for the traveling salesman problem (TSP) than the traditional transiently chaotic neural network. The asymptotic stability and chaotic behavior of the dynamical system with time delay are fully discussed. We not only theoretically prove the existence of Marotto's chaos for the delayed neural network without the cooling schedule by geometrically constructing a transversal homoclinic orbit, but we also discuss the stability of nonautonomous delayed systems using LaSalle's invariance principle. The result of the application to the TSP by the DTCNN might further explain the importance of systems with time delays in the neural system.

Original languageEnglish
Article number033125
JournalChaos
Volume19
Issue number3
DOIs
Publication statusPublished - 2009 Jan 1

Fingerprint

Chaotic Neural Network
Delayed Neural Networks
traveling salesman problem
Travelling salesman problems
Neural networks
Traveling salesman problem
Time Delay
LaSalle's Invariance Principle
Time delay
time lag
Homoclinic Orbit
Global Minimum
Chaotic Behavior
Asymptotic Stability
Cooling
Chaos
Schedule
schedules
Asymptotic stability
Invariance

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Mathematical Physics
  • Physics and Astronomy(all)
  • Applied Mathematics

Cite this

Delayed transiently chaotic neural networks and their application. / Chen, Shyan-Shiou.

In: Chaos, Vol. 19, No. 3, 033125, 01.01.2009.

Research output: Contribution to journalArticle

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