Delay-dependent Robust H<inf>2</inf> guaranteed cost control for singular stochastic neural networks with distributed delays
In this paper, we investigate the problem of global robust asymptotical stability of cellular neural networks with time-varying delays and parameter uncertainties. The parameter uncertainties are assumed to be bounded, the activation functions are supposed to be bounded and globally Lipschitz continuous. Based on the Lyapunov-Krasovskii functional approach, a new delay-dependent stability criteria is presented in terms of linear matrix inequalities (LMIs). The stability criteria can be easily checked by using recently developed algorithms in solving LMIs. Finally, a numerical example is given to illustrate the effectiveness and less conservativeness of our proposed method.