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

Quasi-uniform stability of Caputo-type fractional-order neural networks with mixed delay 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Machine Learning and Cybernetics Springer Journals

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

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2016 by Springer-Verlag Berlin Heidelberg
Subject
Engineering; Computational Intelligence; Artificial Intelligence (incl. Robotics); Control, Robotics, Mechatronics; Complex Systems; Systems Biology; Pattern Recognition
ISSN
1868-8071
eISSN
1868-808X
D.O.I.
10.1007/s13042-016-0523-1
Publisher site
See Article on Publisher Site

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.

Journal

International Journal of Machine Learning and CyberneticsSpringer Journals

Published: Mar 29, 2016

References

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