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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 4/2019

15.11.2017 | Original Article

Nonsmooth exponential synchronization of coupled neural networks with delays: new switching design

verfasst von: Chao Yang, Lihong Huang

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 4/2019

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Abstract

This paper considers the exponential synchronization for a class of coupled time-delayed neural networks with discontinuous activations. Based on differential inclusions theory, set-valued analysis, and by constructing suitable coupling function and Lyapunov function, designing a novel discontinuous controller, when the controller and activation functions are both discontinuous, the global exponential synchronization for the coupled neural networks can be achieved. Especially, we consider a new Lyapunov–Krasovskii functional which is time-dependent, and the results in this paper are applicable to the undirected weighted networks. Finally, to demonstrate the correctness of our results, a numerical example is provided to illustrate it. Our results extend previously known researches.
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Metadaten
Titel
Nonsmooth exponential synchronization of coupled neural networks with delays: new switching design
verfasst von
Chao Yang
Lihong Huang
Publikationsdatum
15.11.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 4/2019
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-017-0742-0

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