Stability analysis for neural networks with time-varying delay
This paper studies the problem of stability analysis for neural networks (NNs) with a time-varying delay. The activation functions are assumed to be neither monotonic, nor differentiable, nor bounded. By defining a more general type of Lyapunov functionals, some new less conservative delay-dependent stability criteria are obtained and shown in terms of linear matrix inequalities (LMIs). Since less variables are involved, the computational complexity of the new conditions is reduced. Numerical examples are given to illustrate the effectiveness and the benefits of the proposed method.