A based-on LMI stability criterion for delayed recurrent neural networks

This paper investigates the stability for a delayed recurrent neural network. Sufficient conditions are obtained for ascertaining the global asymptotic stability of the unique equilibrium of the network based on LMI technique. The results are computationally efficient, since they are in the form of linear matrix inequality (LMI), which can be checked easily by various recently developed convex optimization algorithms. Besides, the analysis approach allows one to consider different types of activation functions, such as piecewise linear, sigmoids with bounded activations as well as C1-smooth sigmoids. In the end of this paper two illustrative examples are also provided to show the effectiveness of our results.