Exponential stability analysis for neutral BAM neural networks with time-varying delays and stochastic disturbances

Abstract

This paper is concerned with the global exponential stability analysis problem for a class of neutral bidirectional associative memory (BAM) neural networks with time-varying delays and stochastic disturbances. The stochastic disturbances are described by state-dependent stochastic processes. By utilizing an appropriately constructed Lyapunov-Krasovskii functional (LKF) and some stochastic analysis approaches, novel delay-dependent conditions are established in terms of linear matrix inequalities (LMIs), which can be easily solved by existing convex optimization techniques. Furthermore, the exponential convergence rate can be estimated based on the obtained results. An illustrate example is given to demonstrate the effectiveness of the proposed methods.