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Asymptotic stability and quantitative synchronization of fractional competitive neural networks with multiple restrictions

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  • Gao, Panqing
  • Ye, Renyu
  • Zhang, Hai
  • Stamova, Ivanka
  • Cao, Jinde

Abstract

The fractional competitive neural networks (FCNNs) with multiple restrictions are considered in this article. Specially, the FCNNs with memristors and time-varying delays in complex field (FCMCNNs) are first introduced, as well as the asymptotic stability (AS) and quantitative synchronization (QTS) issues are investigated. Firstly, applying Filippov’s conclusion theory, a model of FCMCNNs is reduced to a simpler one containing the time-switching memristive coefficients. Secondly, in virtue of the Banach fixed-point theorem, the AS is sufficiently discussed. Thirdly, in the light of the quantized controller (QTC), some concise inequality criteria are drawn to attain the QTS of the master–slave systems. Lastly, by providing two simulation examples, the results obtained manifest the availability and practicality.

Suggested Citation

  • Gao, Panqing & Ye, Renyu & Zhang, Hai & Stamova, Ivanka & Cao, Jinde, 2024. "Asymptotic stability and quantitative synchronization of fractional competitive neural networks with multiple restrictions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 217(C), pages 338-353.
    • Handle: RePEc:eee:matcom:v:217:y:2024:i:c:p:338-353
    DOI: 10.1016/j.matcom.2023.11.005
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