Further result on asymptotic stability criterion of neural networks with time-varying delays
In this paper, the global asymptotic stability problem is dealt with for a class of neural networks (NNs) with time-varying delays. The activation functions are assumed to be neither monotonic, nor differentiable, nor bounded. By constructing an augmented Lyapunov functional which contains an integral term of neuron state vector, an improved delay-dependent stability criterion for delay NNs is established in terms of linear matrix inequalities (LMIs). It is shown that the obtained criterion can provide less conservative results than some existing ones. Numerical examples are given to demonstrate the applicability of the proposed approach.
asymptotic stability neural networks (nns) delay-dependent linear matrix inequality (lmi)