A new stability criterion for discrete-time neural networks: Nonlinear spectral radius

K. L. Mak; J. G. Peng; Z. B. Xu; K. F.C. Yiu

doi:10.1016/j.chaos.2005.09.075

A new stability criterion for discrete-time neural networks: Nonlinear spectral radius

K. L. Mak
, J. G. Peng
, Z. B. Xu
, K. F.C. Yiu

School of Mathematics and Statistics

Research output: Contribution to journal › Article › peer-review

16 Scopus citations

Abstract

In this paper, the exponential stability of nonlinear discrete-time systems is studied. A novel notion of nonlinear spectral radius is defined. Under the assumption of Lipschitz continuity for the activation function, the developed approach is applied to stability analysis of discrete-time neural networks. A series of sufficient conditions for global exponential stability of the neural networks are established and an estimate of the exponential decay rate is also derived for each case.

Original language	English
Pages (from-to)	424-436
Number of pages	13
Journal	Chaos, Solitons and Fractals
Volume	31
Issue number	2
DOIs	https://doi.org/10.1016/j.chaos.2005.09.075
State	Published - Jan 2007

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10.1016/j.chaos.2005.09.075

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title = "A new stability criterion for discrete-time neural networks: Nonlinear spectral radius",

abstract = "In this paper, the exponential stability of nonlinear discrete-time systems is studied. A novel notion of nonlinear spectral radius is defined. Under the assumption of Lipschitz continuity for the activation function, the developed approach is applied to stability analysis of discrete-time neural networks. A series of sufficient conditions for global exponential stability of the neural networks are established and an estimate of the exponential decay rate is also derived for each case.",

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AU - Yiu, K. F.C.

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AB - In this paper, the exponential stability of nonlinear discrete-time systems is studied. A novel notion of nonlinear spectral radius is defined. Under the assumption of Lipschitz continuity for the activation function, the developed approach is applied to stability analysis of discrete-time neural networks. A series of sufficient conditions for global exponential stability of the neural networks are established and an estimate of the exponential decay rate is also derived for each case.

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M3 - 文章

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JO - Chaos, Solitons and Fractals

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IS - 2

ER -

A new stability criterion for discrete-time neural networks: Nonlinear spectral radius

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