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Delay-Dependent Exponential Stability Criterion for BAM Neural Networks with Time-Varying Delays

By employing the Lyapunov stability theory and linear matrix inequality (LMI) technique, delay- dependent stability criterion is derived to ensure the exponential stability of bi-directional associative memory (BAM) neural networks with time-varying delays. The proposed condition can be checked easily by LMI control toolbox in Matlab. A numerical example is given to demonstrate the effectiveness of our results. exponential convergence rate for BAM neural networks with constant or time-varying delays. It will be less conservative if we choose a more suitable Lyapunov functional. In our paper, we develop a new delay-dependent exponential stability condition for BAM neural networks with time-varying delays by utilizing Lyapunov functions. The delay-dependent stability criterion is derived as linear matrix inequality (LMI) that can be efficiently solved by the LMI control toolbox in Matlab. The effectiveness of the proposed stability criterion and its improvement over some existing results are illustrated in numerical example.