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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 12/2018

02.05.2017 | Original Article

Robust extended dissipativity criteria for discrete-time uncertain neural networks with time-varying delays

verfasst von: R. Saravanakumar, Grienggrai Rajchakit, M. Syed Ali, Zhengrong Xiang, Young Hoon Joo

Erschienen in: Neural Computing and Applications | Ausgabe 12/2018

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Abstract

In this draft, we consider the problem of robust extended dissipativity for uncertain discrete-time neural networks (DNNs) with time-varying delays. By constructing appropriate Lyapunov–Krasovskii functional (LKF), sufficient conditions are established to ensure that the considered time-delayed uncertain DNN is extended dissipative. The derived conditions are presented in terms of linear matrix inequalities (LMIs). Numerical examples are provided to illustrate the superiority of this result.
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Metadaten
Titel
Robust extended dissipativity criteria for discrete-time uncertain neural networks with time-varying delays
verfasst von
R. Saravanakumar
Grienggrai Rajchakit
M. Syed Ali
Zhengrong Xiang
Young Hoon Joo
Publikationsdatum
02.05.2017
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 12/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-2974-z

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