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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 6/2018

22.08.2016 | Original Article

Finite-time synchronization of stochastic memristor-based delayed neural networks

verfasst von: Yanchao Shi, Peiyong Zhu

Erschienen in: Neural Computing and Applications | Ausgabe 6/2018

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Abstract

The finite-time synchronization problem of stochastic memristor-based delayed neural network is studied. Certain sufficient conditions are got to assure finite-time synchronization of the chaotic stochastic memristor-based neural networks by using differential inclusions theory, finite-time stability theorem, Lyapunov functional, inequality techniques, stochastic analysis theory and designing a suitable controller. Comparison with previous results, the model of memristor-based neural network of this paper is general, and the given stability conditions are novel. Therefore, the obtained results generalize and improve some existing achievements about the memristor-based neural network. Moreover, a numerical simulation example demonstrates the usefulness of the theoretical results.
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Metadaten
Titel
Finite-time synchronization of stochastic memristor-based delayed neural networks
verfasst von
Yanchao Shi
Peiyong Zhu
Publikationsdatum
22.08.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 6/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2546-7

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