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

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

Applied Mathematics and Computation, 2020, vol. 386, issue C

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.

Keywords: Exponential multistability; Memristive Cohen-Grossberg neural network; Stochastic parameter perturbation; Stable equilibrium point (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:386:y:2020:i:c:s0096300320304422

DOI: 10.1016/j.amc.2020.125483

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