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Global exponential convergence of non-autonomous SICNNs with multi-proportional delays

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Abstract

We give a detailed analysis on convergence for a class of non-autonomous shunting inhibitory cellular neural networks involving multi-proportional delays. By employing the differential inequality techniques, we establish a novel result to ensure that all solutions of the addressed system converge exponentially to zero vector. Moreover, numerical simulations supporting our theoretical results are also included.

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Acknowlwdment

This work was supported by the Construction Program of the Key Discipline in Hunan University of Arts and Science–Applied Mathematics.

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Authors and Affiliations

  1. College of Mathematics and Computer Science, Hunan University of Arts and Science, Changde, 415000, Hunan, People’s Republic of China

    Bingwen Liu

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  1. Bingwen Liu
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Correspondence to Bingwen Liu.

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Liu, B. Global exponential convergence of non-autonomous SICNNs with multi-proportional delays. Neural Comput & Applic 28, 1927–1931 (2017). https://doi.org/10.1007/s00521-015-2165-8

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  • DOI: https://doi.org/10.1007/s00521-015-2165-8

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