Exponential stability for time-delay neural networks via new weighted integral inequalities

TL;DR

This paper introduces a new weighted integral inequality to analyze exponential stability in time-delay neural networks, enhancing stability criteria with numerical validation.

Contribution

It develops a novel integral inequality using weighted orthogonal functions, extending existing methods for stability analysis of neural networks with delays.

Findings

New integral inequality improves stability analysis.

Enhanced criteria for exponential stability.

Numerical examples verify the effectiveness.

Abstract

We study exponential stability for a kind of neural networks having time-varying delay. By extending the auxiliary function-based integral inequality, a novel integral inequality is derived by using weighted orthogonal functions of which one is discontinuous. Then, the new inequality is applied to investigate the exponential stability of time-delay neural networks via Lyapunov-Krasovskii functional (LKF) method. Numerical examples are given to verify the advantages of the proposed criterion.