On Global Stability of Delayed BAM Stochastic Neural Networks with Markovian Switching

Authors:
Yurong Liu;Zidong Wang;Xiaohui Liu
Affiliations:
Department of Mathematics, Yangzhou University, Yangzhou, People's Republic of China 225002;Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex, UK UB8 3PH;Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex, UK UB8 3PH
Venue:
Neural Processing Letters
Year:
2009

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Abstract

In this paper, the stability analysis problem is investigated for stochastic bi-directional associative memory (BAM) neural networks with Markovian jumping parameters and mixed time delays. Both the global asymptotic stability and global exponential stability are dealt with. The mixed time delays consist of both the discrete delays and the distributed delays. Without assuming the symmetry of synaptic connection weights and the monotonicity and differentiability of activation functions, we employ the Lyapunov---Krasovskii stability theory and the Itô differential rule to establish sufficient conditions for the delayed BAM networks to be stochastically globally exponentially stable and stochastically globally asymptotically stable, respectively. These conditions are expressed in terms of the feasibility to a set of linear matrix inequalities (LMIs). Therefore, the global stability of the delayed BAM with Markovian jumping parameters can be easily checked by utilizing the numerically efficient Matlab LMI toolbox. A simple example is exploited to show the usefulness of the derived LMI-based stability conditions.