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Finite-time stability of fractional-order bidirectional associative memory neural networks with mixed time-varying delays

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Abstract

This paper investigates the finite-time stability of fractional-order bidirectional associative memory neural networks with mixed time-varying delays. The sufficient conditions are derived to ensure the finite-time stability of systems by employing some analytical techniques and some inequalities. In addition, some conditions are achieved to guarantee the existence, the uniqueness and the finite-time stability of equilibrium point. Finally, two numerical examples are given to verify the effectiveness of the obtained main results.

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Acknowledgements

The first author would like to express her sincere gratitude to Professor Xiaoqun Wu (Wuhan University) for her kind help. The research is supported by the National Natural Science Foundation of China (Grant No. 11401595).

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  1. School of Mathematics and Statistics, South-Central University for Nationalities, Wuhan, 430074, Hubei, China

    Zhanying Yang, Jie Zhang & Yanqing Niu

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  1. Zhanying Yang
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  2. Jie Zhang
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Correspondence to Zhanying Yang.

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Yang, Z., Zhang, J. & Niu, Y. Finite-time stability of fractional-order bidirectional associative memory neural networks with mixed time-varying delays. J. Appl. Math. Comput. 63, 501–522 (2020). https://doi.org/10.1007/s12190-020-01327-6

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  • DOI: https://doi.org/10.1007/s12190-020-01327-6

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