Stability analysis for stochastic BAM neural networks with Markovian jumping parameters

Authors:
Guanjun Wang;Jinde Cao;Ming Xu
Affiliations:
Department of Mathematics, Southeast University, Nanjing 210096, China;Department of Mathematics, Southeast University, Nanjing 210096, China;Department of Mathematics, Ji'nan University, Guangzhou 510632, China
Venue:
Neurocomputing
Year:
2009

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Abstract

This paper is concerned with the stability analysis issue for stochastic delayed bidirectional associative memory (BAM) neural network with Markovian jumping parameters. Assume that the jumping parameters are generated from continue-time discrete-state homogeneous Markov process and the delays are time-invariant. By employing the Lyapunov stability theory, some inequality techniques and the stochastic analysis, sufficient conditions are derived to achieve the global exponential stability in the mean square of the stochastic BAM neural network. One example is also provided in the end of this paper to illustrate the effectiveness of our results.