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29-03-2016 | Original Article | Issue 5/2017

Quasi-uniform stability of Caputo-type fractional-order neural networks with mixed delay

Journal:: International Journal of Machine Learning and Cybernetics > Issue 5/2017

Authors:: Huaiqin Wu, Xinxin Zhang, Shunhui Xue, Peifeng Niu

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Important notes

This work was jointly supported by the National Natural Science Foundation of China (61573306), the Postgraduate Innovation Project of Hebei province of China (00302-6370019) and High level talent support project of Hebei province of China (C2015003054).

Abstract

In this paper, a class of Caputo-type fractional-order neural networks with mixed delay is introduced. By employing known inequalities, such as Hölder inequality, Cauchy–Schwartz inequality and Gronwall inequality, sufficient conditions are presented to ensure that such neural network is quasi-uniformly stable. Finally, a numerical example is presented to prove the theoretical results.

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Title: Quasi-uniform stability of Caputo-type fractional-order neural networks with mixed delay
Authors:: Huaiqin Wu
Xinxin Zhang
Shunhui Xue
Peifeng Niu
Publication date: 29-03-2016
DOI: https://doi.org/10.1007/s13042-016-0523-1
Publisher: Springer Berlin Heidelberg
Journal: International Journal of Machine Learning and Cybernetics
Issue 5/2017
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X

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