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

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  • Zhang, Zhongjie
  • Yu, Tingting
  • Zhang, Xian

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.

Suggested Citation

  • Zhang, Zhongjie & Yu, Tingting & Zhang, Xian, 2022. "Algebra criteria for global exponential stability of multiple time-varying delay Cohen–Grossberg neural networks," Applied Mathematics and Computation, Elsevier, vol. 435(C).
    • Handle: RePEc:eee:apmaco:v:435:y:2022:i:c:s0096300322005355
    DOI: 10.1016/j.amc.2022.127461
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    References listed on IDEAS

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