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Neural Processing Letters
Erschienen in: Neural Processing Letters 3/2017

07.04.2017

Asymptotic Stability of Cohen–Grossberg BAM Neutral Type Neural Networks with Distributed Time Varying Delays

verfasst von: M. Syed Ali, S. Saravanan, M. Esther Rani, S. Elakkia, Jinde Cao, Ahmed Alsaedi, Tasawar Hayat

Erschienen in: Neural Processing Letters | Ausgabe 3/2017

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Abstract

This paper is concerned with the problem of asymptotic stability of neutral type Cohen–Grossberg BAM neural networks with discrete and distributed time-varying delays. By constructing a suitable Lyapunov–Krasovskii functional (LKF), reciprocal convex technique and Jensen’s inequality are used to delay-dependent conditions are established to analysis the asymptotic stability of Cohen–Grossberg BAM neural networks with discrete and distributed time-varying delays. These stability conditions are formulated as linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms. Finally numerical examples are given to illustrate the usefulness of our proposed method.
Vorheriger Artikel Adaptive Synchronization of Stochastic Memristor-Based Neural Networks with Mixed Delays
Nächster Artikel Robust State Estimation for Delayed Complex-Valued Neural Networks

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Metadaten
Titel
Asymptotic Stability of Cohen–Grossberg BAM Neutral Type Neural Networks with Distributed Time Varying Delays
verfasst von
M. Syed Ali
S. Saravanan
M. Esther Rani
S. Elakkia
Jinde Cao
Ahmed Alsaedi
Tasawar Hayat
Publikationsdatum
07.04.2017
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2017
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-017-9622-6

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