Robust stability of discrete-time uncertain stochastic BAM neural networks with time-varying delays

In this paper, the global exponential stability is investigated for the discrete-time uncertain stochastic bidirectional associate memory neural networks with time-varying delays. For the neural networks under study, a generalized activation function is considered, and the traditional assumptions on the boundedness, monotony and differentiability of the activation functions are removed. By utilizing suitable Lyapunov-Krasovsky functional and using stochastic analysis theory and inequality technique, several sufficient conditions for checking the global robust exponential stability of the addressed neural networks are obtained in terms of linear matrix inequalities (LMIs), which can be checked numerically using the effective LMI toolbox in MATLAB. An example is given to show the effectiveness and less conservatism of the proposed criteria.