Exponential multistability of memristive Cohen-Grossberg neural networks with stochastic parameter perturbations

Wei Yao, Chunhua Wang, Yichuang Sun, Chao Zhou, Hairong Lin

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15 Citations (Scopus)
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Abstract

Due to instability being induced easily by parameter disturbances of network systems, this paper investigates the multistability of memristive Cohen-Grossberg neural networks (MCGNNs) under stochastic parameter perturbations. It is demonstrated that stable equilibrium points of MCGNNs can be flexibly located in the odd-sequence or even-sequence regions. Some sufficient conditions are derived to ensure the exponential multistability of MCGNNs under parameter perturbations. It is found that there exist at least (w+2) l (or (w+1) l) exponentially stable equilibrium points in the odd-sequence (or the even-sequence) regions. In the paper, two numerical examples are given to verify the correctness and effectiveness of the obtained results.
Original languageEnglish
Article number125483
Number of pages18
JournalApplied Mathematics and Computation
Volume386
Early online date26 Jun 2020
DOIs
Publication statusPublished - 1 Dec 2020

Keywords

  • Exponential multistability
  • Memristive Cohen-Grossberg neural network
  • Stable equilibrium point
  • Stochastic parameter perturbation

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