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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 3-4/2013

01.09.2013 | Original Article

Dynamic behaviors of memristor-based delayed recurrent networks

verfasst von: Shiping Wen, Zhigang Zeng, Tingwen Huang

Erschienen in: Neural Computing and Applications | Ausgabe 3-4/2013

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Abstract

This paper investigates the problem of the existence and global exponential stability of the periodic solution of memristor-based delayed network. Based on the knowledge of memristor and recurrent neural network, the model of the memristor-based recurrent networks is established. Several sufficient conditions are obtained, which ensure the existence of periodic solutions and global exponential stability of the memristor-based delayed recurrent networks. These results ensure global exponential stability of memristor-based network in the sense of Filippov solutions. And, it is convenient to estimate the exponential convergence rates of this network by the results. An illustrative example is given to show the effectiveness of the theoretical results.
Vorheriger Artikel RETRACTED ARTICLE: Artificial neural networks for prediction Charpy impact energy of Al6061/SiCp-laminated nanocomposites
Nächster Artikel Empirical modeling of shear strength of steel fiber reinforced concrete beams by gene expression programming

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Metadaten
Titel
Dynamic behaviors of memristor-based delayed recurrent networks
verfasst von
Shiping Wen
Zhigang Zeng
Tingwen Huang
Publikationsdatum
01.09.2013
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 3-4/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-012-0998-y

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