Algebra criteria for global exponential stability of multiple time-varying delay Cohen–Grossberg neural networks

Zhongjie Zhang, Tingting Yu and Xian Zhang

Applied Mathematics and Computation, 2022, vol. 435, issue C

Abstract: This paper aims at establishing global exponential stability criteria for multiple time-varying delay Cohen–Grossberg neural networks (CGNNs). The considered network models cannot be expressed as the vector-matrix form, which yields that many methods in literature are unavailable. By constructing novel Lyapunov–Krasovskii functionals, two novel algebraic criteria guaranteeing global exponential stability of CGNNs under consideration are given. A pair of numerical examples are used to explain the effectiveness of the obtained algebra criteria relative to the previously stability conditions. It is worth emphasizing that the approach applied in this paper is applicable to CGNNs that may or may not be represented in vector-matrix form.

Keywords: Global exponential stability; Algebra Criteria; Cohen–Grossberg neural networks; Multiple time-varying delays; Lyapunov–Krasovskii functionals (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:435:y:2022:i:c:s0096300322005355

DOI: 10.1016/j.amc.2022.127461

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