Passivity analysis of memristor-based complex-valued neural networks with time-varying delays

Velmurugan, G, Rakkiyappan, R and Lakshmanan, S 2015, Passivity analysis of memristor-based complex-valued neural networks with time-varying delays, Neural processing letters, vol. 42, no. 3, pp. 517-540, doi: 10.1007/s11063-014-9371-8.

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Title Passivity analysis of memristor-based complex-valued neural networks with time-varying delays
Author(s) Velmurugan, G
Rakkiyappan, R
Lakshmanan, SORCID iD for Lakshmanan, S orcid.org/0000-0002-4622-3782
Journal name Neural processing letters
Volume number 42
Issue number 3
Start page 517
End page 540
Total pages 24
Publisher Springer New York
Place of publication N.Y. United States
Publication date 2015-12
ISSN 1370-4621
1573-773X
Keyword(s) memristors passivity CVNNs
linear matrix inequality
time-varying delays
Summary In this paper, the model of memristor-based complex-valued neural networks (MCVNNs) with time-varying delays is established and the problem of passivity analysis for MCVNNs is considered and extensively investigated. The analysis in this paper employs results from the theory of differential equations with discontinuous right-hand side as introduced by Filippov. By employing the appropriate Lyapunov–Krasovskii functional, differential inclusion theory and linear matrix inequality (LMI) approach, some new sufficient conditions for the passivity of the given MCVNNs are obtained in terms of both complex-valued and real-value LMIs, which can be easily solved by using standard numerical algorithms. Numerical examples are provided to illustrate the effectiveness of our theoretical results.
Language eng
DOI 10.1007/s11063-014-9371-8
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
0801 Artificial Intelligence And Image Processing
1702 Cognitive Science
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Springer Science & Business Media
Persistent URL http://hdl.handle.net/10536/DRO/DU:30077156

Document type: Journal Article
Collections: Centre for Intelligent Systems Research
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