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

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        AMC_Exponential_multistability_of_MCGNNs_.pdf (PDF, 459Kb)(embargoed until 26/06/2021)
        Author
        Yao, Wei
        Wang, Chunhua
        Sun, Yichuang
        Zhou, Chao
        Lin, Hairong
        Attention
        2299/23009
        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.
        Publication date
        2020-12-01
        Published in
        Applied Mathematics and Computation
        Published version
        https://doi.org/10.1016/j.amc.2020.125483
        Other links
        http://hdl.handle.net/2299/23009
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