Stability analysis of generalized neural networks with fast-varying delay via a relaxed negative-determination quadratic function method

This paper studies the problem of stability analysis of generalized neural networks (GNN) with a fast-varying delay. Firstly, an improved augmented Lyapunov-Krasovskii func-tional (LKF) is proposed by fully considering more states information about interrelated systems and neuron activation function conditions. Then, to handle the derivative of the LKF, the generalized reciprocally convex combination and a relaxed quadratic function negative-determination are employed. Based on these methods and the augmented LKF, a less conservative delay-dependent stability criterion for GNN with a fast-varying delay is presented. Finally, some numerical examples are given to demonstrate the effective superiority of the proposed criterion. (c) 2020 Elsevier Inc. All rights reserved.

Automated summary

This paper introduces an improved augmented Lyapunov-Krasovskii functional to address the stability analysis issue of generalized neural networks with fast-varying delay. By utilizing specific methods and strategies based on the augmented LKF, a less conservative delay-dependent stability criterion is proposed. Numerical examples demonstrate the effectiveness of this criterion.