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Neural Computing and Applications

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01.04.2016 | Original Article

Global dissipativity of memristor-based complex-valued neural networks with time-varying delays

verfasst von: R. Rakkiyappan, G. Velmurugan, Xiaodi Li, Donal O’Regan

Erschienen in: Neural Computing and Applications | Ausgabe 3/2016

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Abstract

Memristor is the new model two-terminal nonlinear circuit device in electronic circuit theory. This paper deals with the problem of global dissipativity and global exponential dissipativity for memristor-based complex-valued neural networks (MCVNNs) with time-varying delays. Sufficient global dissipativity conditions are derived from the theory of M-matrix analysis, and the globally attractive set as well as the positive invariant set is established. By constructing Lyapunov–Krasovskii functionals and using a linear matrix inequality technique, some new sufficient conditions on global dissipativity and global exponential dissipativity of MCVNNs are derived. Finally, two numerical examples are presented to demonstrate the effectiveness of our proposed theoretical results.

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Titel: Global dissipativity of memristor-based complex-valued neural networks with time-varying delays
verfasst von: R. Rakkiyappan
G. Velmurugan
Xiaodi Li
Donal O’Regan
Publikationsdatum: 01.04.2016
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 3/2016
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-015-1883-2

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