Global exponential stability and periodic oscillations of reaction-diffusion BAM neural networks with periodic coefficients and general delays

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

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Detail(s)

Original languageEnglish
Pages (from-to)129-142
Journal / PublicationInternational Journal of Bifurcation and Chaos
Volume17
Issue number1
Publication statusPublished - Jan 2007

Abstract

For a large class of reaction diffusion bidirectional associative memory (RDBAM) neural networks with periodic coefficients and general delays, several new delay-dependent or delay-independent sufficient conditions ensuring the existence and global exponential stability of a unique periodic solution are given, by constructing suitable Lyapunov functional and employing some analytic techniques such as Poincaré mapping. The presented conditions arc easily verifiable and useful in the design and applications of RDBAM neural networks. Moreover, the employed analytic techniques do not require the symmetry of the bidirectional connection weight matrix, the boundedness, monotonicity and differentiability of activation functions of the network. In several ways, the results generalize and improve those established in the current-literature. © World Scientific Publishing Company.

Research Area(s)

  • Bidirectional neural network, General delay, Global exponential stability, Lyapunov functional, Periodic coefficient, Periodic oscillation, Poincare mapping, Reaction-diffusion

Citation Format(s)

Global exponential stability and periodic oscillations of reaction-diffusion BAM neural networks with periodic coefficients and general delays. / Zhou, Qinghua; Sun, Jianhua; Chen, Guanrong.

In: International Journal of Bifurcation and Chaos, Vol. 17, No. 1, 01.2007, p. 129-142.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review