Delay-dependent global asymptotic stability for delayed cellular neural networks

DOI10.1016/j.cnsns.2008.04.012zbMath1221.37194OpenAlexW2082794226MaRDI QIDQ716627

Hui Yu, Yan-Jun Shen, Ji-Gui Jian

Publication date: 30 September 2011

Published in: Communications in Nonlinear Science and Numerical Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cnsns.2008.04.012

zbMATH Keywords

linear matrix inequalitytime-delayglobal asymptotic stabilitydelay-dependent criteria

Mathematics Subject Classification ID

Dynamical systems in biology (37N25) Neural networks for/in biological studies, artificial life and related topics (92B20) Stability theory of functional-differential equations (34K20)

Related Items (6)

Dynamical analysis of impulsive neural networks with time-varying delays ⋮ Dynamical Behavior of Cooperative Supportive System Involving Intra-Network Delays in Information Propagation ⋮ An oscillatory criterion for a time delayed neural ring network model ⋮ General impulsive control of chaotic systems based on a TS fuzzy model ⋮ Stability analysis of uncertain fuzzy Hopfield neural networks with time delays ⋮ Existence and global stability of a periodic solution for a cellular neural network

Uses Software

• LMI toolbox

Cites Work

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• New results on stability analysis of neural networks with time-varying delays
• Global asymptotic stability of a larger class of neural networks with constant time delay
• A new approach to exponential stability analysis of neural networks with time-varying delays
• On stability of cellular neural networks with delay
• Linear Matrix Inequalities in System and Control Theory
• Global stability conditions for delayed CNNs
• Robust stability analysis and stabilization for uncertain linear neutral delay systems
• A Less Conservative Robust Stability Test for Linear Uncertain Time-Delay Systems
• New Exponential Estimates for Time-Delay Systems
• LMI-based approach for asymptotically stability analysis of delayed neural networks
• An improved global stability result for delayed cellular neural networks
• Self-organizing maps.

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