Improved Robust Passive Criteria of Neural Networks with Discrete and Distributed Delays Based on Extended Reciprocally Convex Matrix Inequality

This paper investigates the passive problem of neural networks with discrete and distributed delays. At first, a novel Lyapunov-Krasovskii functional (LKF) is constructed via introducing a delay-product-type term such that the delay change rate information is abundantly considered. Then, an extended reciprocally convex matrix inequality combined with the Wirtinger-based integral inequality with less conservatism is employed to realize the tight estimation for the derivative of the LKF. As a result, two improved passive criteria for the neural networks with discrete and distributed delays are presented. Finally, two numerical examples are given to show the effectiveness and improvements of our methods.

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