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

M. Syed Ali; S. Saravanan; M. Esther Rani; S. Elakkia; Jinde Cao; Ahmed Alsaedi; Tasawar Hayat

doi:10.1007/s11063-017-9622-6

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

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

Thiruvalluvar University
Southeast University, Nanjing
Faculty of Sciences, King Abdulaziz University
Quaid-I-Azam University

Research output: Contribution to journal › Article › peer-review

31 Scopus citations

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.

Original language	English
Pages (from-to)	991-1007
Number of pages	17
Journal	Neural Processing Letters
Volume	46
Issue number	3
DOIs	https://doi.org/10.1007/s11063-017-9622-6
State	Published - 1 Dec 2017
Externally published	Yes

Keywords

Cohen–Grossberg neural networks
Linear matrix inequality
Lyapunov–Krasovskii functional
Neutral-delay

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10.1007/s11063-017-9622-6

Cite this

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

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{\textquoteright}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.",

keywords = "Cohen–Grossberg neural networks, Linear matrix inequality, Lyapunov–Krasovskii functional, Neutral-delay",

author = "Ali, \{M. Syed\} and S. Saravanan and Rani, \{M. Esther\} and S. Elakkia and Jinde Cao and Ahmed Alsaedi and Tasawar Hayat",

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AU - Ali, M. Syed

AU - Saravanan, S.

AU - Rani, M. Esther

AU - Elakkia, S.

AU - Cao, Jinde

AU - Alsaedi, Ahmed

AU - Hayat, Tasawar

PY - 2017/12/1

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N2 - 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.

AB - 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.

KW - Cohen–Grossberg neural networks

KW - Linear matrix inequality

KW - Lyapunov–Krasovskii functional

KW - Neutral-delay

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SP - 991

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JO - Neural Processing Letters

JF - Neural Processing Letters

IS - 3

ER -

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

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Keywords

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