Stabilization of inertial Cohen-Grossberg neural networks with generalized delays: A direct analysis approach

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Journal Article
Chaos, Solitons and Fractals, 2021, 142, pp. 1-9
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The paper is mainly devoted to the stabilization problem of Cohen-Grossberg type inertial neural networks (INNs) with generalized delays by developing a direct analysis approach to replace the previous transformations of reduced order. Above all, a generalized form of time delays is developed to unify discrete constant delays, discrete variable delays and proportional delays. In stabilization analysis, in the absence of variable substitutions, a direct method is proposed by constructing Lyapunov functionals and designing control schemes for the addressed second-order Cohen-Grossberg INNs to achieve asymptotical or adaptive stabilization. The obtained criteria are simpler and more easily verified in applications compared with the related existing results. At last, three specified examples are provided to verify the theoretical results.
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