Almost sure exponential stability of recurrent neural networks with Markovian switching

Yi Shen, Jun Wang

Research output: Contribution to journalArticlepeer-review

145 Citations (Scopus)

Abstract

This paper presents new stability results for recurrent neural networks with Markovian switching. First, algebraic criteria for the almost sure exponential stability of recurrent neural networks with Markovian switching and without time delays are derived. The results show that the almost sure exponential stability of such a neural network does not require the stability of the neural network at every individual parametric configuration. Next, both delay-dependent and delay-independent criteria for the almost sure exponential stability of recurrent neural networks with time-varying delays and Markovian-switching parameters are derived by means of a generalized stochastic Halanay inequality. The results herein include existing ones for recurrent neural networks without Markovian switching as special cases. Finally, simulation results in three numerical examples are discussed to illustrate the theoretical results.

Original languageEnglish
Pages (from-to)840-855
Number of pages16
JournalIEEE Transactions on Neural Networks
Volume20
Issue number5
DOIs
Publication statusPublished - 2009
Externally publishedYes

Keywords

  • Almost sure exponential stability
  • Halanay inequality
  • Markov chain
  • Recurrent neural networks
  • Time-varying delay

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