Necessary and sufficient condition for the absolute exponential stability of a class of neural networks with finite delay

DOI10.1016/j.physleta.2005.11.038zbMath1187.34100OpenAlexW2075385310WikidataQ61891390 ScholiaQ61891390MaRDI QIDQ973553

Tingwen Huang, Chuandong Li, Cao, Jinde

Publication date: 2 June 2010

Published in: Physics Letters. A (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.physleta.2005.11.038

zbMATH Keywords

exponential stabilityneural networksM-matrixnecessary and sufficient conditionfinite delayabsolute exponential stability

Mathematics Subject Classification ID

Lyapunov and other classical stabilities (Lagrange, Poisson, (L^p, l^p), etc.) in control theory (93D05) Stability theory of functional-differential equations (34K20) Dynamical systems in control (37N35)

Related Items (9)

On \(p\)th moment exponential stability of stochastic fuzzy cellular neural networks with time-varying delays and impulses ⋮ Dynamical behaviors of fuzzy cellular neural networks with discrete time delays ⋮ Global asymptotic stability of fuzzy cellular neural networks with time-varying delays ⋮ Synchronization for Markovian coupled neural networks with partial mode observation: the finite-time case ⋮ Synchronized stability in a reaction-diffusion neural network model ⋮ Convergence dynamics of Cohen-Grossberg neural networks with continuously distributed delays ⋮ Analysis of global exponential stability and periodic solutions of FCNNs with constant delays and~time-varying delays ⋮ Dynamical behaviors of fuzzy reaction-diffusion periodic cellular neural networks with variable coefficients and delays ⋮ A domain attraction criterion for interval fuzzy neural networks

Cites Work

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• Absolute exponential stability of recurrent neural networks with Lipschitz-continuous activation functions and time delays
• A condition for global convergence of a class of symmetric neural circuits
• Necessary and sufficient condition for absolute stability of neural networks
• A simple proof of a necessary and sufficient condition for absolute stability of symmetric neural networks
• New conditions for global stability of neural networks with application to linear and quadratic programming problems

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