Improved delay-dependent stability criteria for recurrent neural networks with time-varying delays

This paper is concerned with the problem of delay-dependent stability criteria for recurrent neural networks with time-varying delays. A new class of Lyapunov functional is introduced by decomposing the delays in all integral terms. By exploiting all possible information in various delay intervals and using reciprocally convex approach, some less conservative stability criteria are obtained in terms of linear matrix inequalities (LMIs). Finally, two numerical examples are given to illustrate the effectiveness of the derived results.