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

Author

Listed:
  • Yao, Wei
  • Wang, Chunhua
  • Sun, Yichuang
  • Zhou, Chao
  • Lin, Hairong

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.

Suggested Citation

  • Yao, Wei & Wang, Chunhua & Sun, Yichuang & Zhou, Chao & Lin, Hairong, 2020. "Exponential multistability of memristive Cohen-Grossberg neural networks with stochastic parameter perturbations," Applied Mathematics and Computation, Elsevier, vol. 386(C).
    • Handle: RePEc:eee:apmaco:v:386:y:2020:i:c:s0096300320304422
    DOI: 10.1016/j.amc.2020.125483
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    References listed on IDEAS

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    Cited by:

    1. Yu, Fei & Shen, Hui & Zhang, Zinan & Huang, Yuanyuan & Cai, Shuo & Du, Sichun, 2021. "Dynamics analysis, hardware implementation and engineering applications of novel multi-style attractors in a neural network under electromagnetic radiation," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    2. Zhou, Chao & Wang, Chunhua & Yao, Wei & Lin, Hairong, 2022. "Observer-based synchronization of memristive neural networks under DoS attacks and actuator saturation and its application to image encryption," Applied Mathematics and Computation, Elsevier, vol. 425(C).

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