Further analysis of weighted integral inequalities for improved exponential stability analysis of time delay neural networks systems

2024/12/11 by Yuanyuan Zhang, Han Xue, Zhang, Yuanyuan +7
Computer Science · #Neural Networks and Applications #Machine Learning and ELM #Neural Networks Stability and Synchronization

paper · pdf · doi:10.48550/arxiv.2412.08172

Abstract

This work investigates the exponential stability of neural networks (NNs) systems with time delays. By considering orthogonal polynomials with weighted terms, a new weighted integral inequality is presented. This inequality extend several recently established results. Additionally, based on the reciprocally convex inequality, this study focuses on analyzing the exponential stability of systems with time-varying delays that include an exponential decay factor, a weighted version of the reciprocally convex inequality is first derived. Utilizing these inequalities and the suitable Lyapunov-Krasovskii functionals (LKFs) within the framework of linear matrix inequalities (LMIs), the new criteria for the exponential stability of NNs system is obtained. The effectiveness of the proposed method is demonstrated through multiple numerical examples.

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