Dual delay-partitioning approach to stability analysis of generalized neural networks with interval time-varying delay

Abstract

This paper is concerned with improved delay-dependent stability criteria for generalized neural networks (GNNs) with interval time-varying delay. A dual delay-partitioning approach is introduced to partition the delay intervals [0,τa] and [τa,τb] into different multi-segments separately. A newly augmented Lyapunov–Krasovskii functional (LKF) with triple integral terms is constructed by dual–partitioning the delay in integral terms, in which the relationships between the augmented state vectors are fully taken into account. The Wirtinger-based integral inequality and Peng-Park's integral inequality are employed to effectively handle the cross-product terms occurred in derivative of the LKF. Therefore, less conservative results can be achieved in terms of es and LMIs. Finally, two numerical examples are included to show that the deduced criteria are less conservative than existing ones.