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Neural Processing Letters

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07-04-2017

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

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

Published in: Neural Processing Letters | Issue 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.

previous article Adaptive Synchronization of Stochastic Memristor-Based Neural Networks with Mixed Delays

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Title: Asymptotic Stability of Cohen–Grossberg BAM Neutral Type Neural Networks with Distributed Time Varying Delays
Authors: M. Syed Ali
S. Saravanan
M. Esther Rani
S. Elakkia
Jinde Cao
Ahmed Alsaedi
Tasawar Hayat
Publication date: 07-04-2017
Publisher: Springer US
Published in: Neural Processing Letters / Issue 3/2017
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI: https://doi.org/10.1007/s11063-017-9622-6

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