New approaches on stability criteria for neural networks with interval time-varying delays

This paper concerns the problem of delay-dependent stability criteria for neural networks with interval time-varying delays. First, by constructing a newly augmented Lyapunov-Krasovskii functional and combining with a reciprocally convex combination technique, less conservative stability criterion is established in terms of linear matrix inequalities (LMIs), which will be introduced in Theorem 1. Second, by taking different interval of integral terms of Lyapunov-Krasovskii functional utilized in Theorem 1, further improved stability criterion is proposed in Theorem 2. Third, a novel approach which divides the bounding of activation function into two subinterval are proposed in Theorem 3 to reduce the conservatism of stability criterion. Finally, through two well-known numerical examples used in other literature, it will be shown the proposed stability criteria achieves the improvements over the existing ones and the effectiveness of the proposed idea.