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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 2/2015

01.04.2015 | Original Article

Adaptive exponential synchronization of delayed Cohen–Grossberg neural networks with discontinuous activations

verfasst von: Huaiqin Wu, Xiaowei Zhang, Ruoxia Li, Rong Yao

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 2/2015

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Abstract

This paper treats of the exponential synchronization issue of delayed Cohen–Grossberg neural networks with discontinuous activations. By utilizing Lyapunov stability theory, an adaptive controller is designed such that the response system can be exponentially synchronized with a drive system. Our synchronization criteria are easily verified and the obtained results are also applicable to neural networks with continuous activations since they are a special case of neural networks with discontinuous activations. Results of this paper improve a few previous known results. Finally, numerical simulations are given to verify the effectiveness of the theoretical results.
Vorheriger Artikel Visual reasoning for discoveries
Nächster Artikel User feedback based metasearching using neural network

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Metadaten
Titel
Adaptive exponential synchronization of delayed Cohen–Grossberg neural networks with discontinuous activations
verfasst von
Huaiqin Wu
Xiaowei Zhang
Ruoxia Li
Rong Yao
Publikationsdatum
01.04.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 2/2015
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-014-0258-9

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