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New criteria on the finite-time stability of fractional-order BAM neural networks with time delay

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Abstract

In this paper, the finite-time stability of a class of fractional-order bidirectional associative memory neural networks(FBAMNNs) with time delay is concerned. Based on the monotonicity of function, a new inequality is proved. For \(0< \alpha < 1\) and \(1< \alpha < 2\), based on the properties of the fractional derivative, the method of step and the fractional Gronwall inequality or the generalized Gronwall inequality, some new criteria on the finite-time stability of FBAMNNs are derived. Finally, three numerical examples are provided to show the effectiveness and superiority of the criteria obtained in this paper.

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Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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Funding

The National Natural Science Foundation of China under grant Nos. 61773404 and 61271355 and Fundamental Research Funds for the Central Universities of Central South University No. 2018zzts007.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Central South University, Changsha, 410083, Hunan, China

    Xuemei Li, Xinge Liu & Shuailei Zhang

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  1. Xuemei Li
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  2. Xinge Liu
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  3. Shuailei Zhang
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All authors contributed equally and significantly in writing this paper. All authors read and approved the final manuscript.

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Correspondence to Xinge Liu.

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Li, X., Liu, X. & Zhang, S. New criteria on the finite-time stability of fractional-order BAM neural networks with time delay. Neural Comput & Applic 34, 4501–4517 (2022). https://doi.org/10.1007/s00521-021-06605-3

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