Global exponential periodicity and global exponential stability of a class of recurrent neural networks

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

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)36-48
Journal / PublicationPhysics Letters, Section A: General, Atomic and Solid State Physics
Volume329
Issue number1-2
Publication statusPublished - 16 Aug 2004
Externally publishedYes

Abstract

Some sufficient criteria have been given ensuring existence, uniqueness and global exponential stability of periodic solution of a class of recurrent neural network (RNN) model by using the comparison principle, the theory of monotone flow and monotone operator. The conditions are very viable in some applied fields. For instance, they can be applied to design globally exponentially stable RNNs and periodic oscillatory RNNs and easily checked in practice. In addition, we provide a new and efficacious method for the qualitative analysis of neural networks. © 2004 Elsevier B.V.

Research Area(s)

  • 43.80.+p, 85.40.Ls, 87.10.+e