 Novel stability criteria of generalized neural networks with time-varying delay based on the same augmented LKF and bounding techniqueZhengliang Zhai, Huaicheng Yan, Shiming Chen, Yufang Chang and Jing Zhou Applied Mathematics and Computation, 2024, vol. 460, issue C Abstract: This paper researches the stability issue of generalized neural networks (GNN) with time-varying delay. For the delay, its derivative has an upper bound or is unknown. Firstly, the augmented Lyapunov-Krasovskii functional (LKF) is constructed based on the state vectors of the third order integral inequalities. Then, by introducing two sets of state vectors, the LKF derivative is presented as the quadratic and quintic polynomials of the delay, respectively. Next, the new quadratic and quintic polynomial negative definite conditions (NDCs) are proposed to set up the linear matrix inequalities (LMIs). In addition, based on the same LKF and third order integral inequalities, this paper proves that the introduction of extra state vectors increases the conservatism of the derived stability conditions. Eventually, the advantages of the provided conditions are illustrated by several numerical examples. Keywords: Third order integral inequality; Negative definite conditions; Generalized neural networks; Time-varying delay (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:460:y:2024:i:c:s0096300323004587 DOI: 10.1016/j.amc.2023.128289 Access Statistics for this article Applied Mathematics and Computation is currently edited by Theodore Simos More articles in Applied Mathematics and Computation from Elsevier |
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