New LMI conditions for global exponential stability of cellular neural networks with delays

DOI10.1016/j.nonrwa.2007.09.011zbMath1154.34378OpenAlexW2051688095MaRDI QIDQ1003277

Hongyong Zhao, Ling Chen

Publication date: 27 February 2009

Published in: Nonlinear Analysis. Real World Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.nonrwa.2007.09.011

zbMATH Keywords

linear matrix inequalityglobal exponential stabilityequilibrium point

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Stability theory of functional-differential equations (34K20) Cell biology (92C37) Global stability of solutions to ordinary differential equations (34D23)

Related Items (max. 100)

EXPONENTIAL STABILITY OF STOCHASTIC FUZZY CELLULAR NEURAL NETWORKS WITH DISTRIBUTED DELAYS ⋮ Robust stability analysis of impulsive complex-valued neural networks with time delays and parameter uncertainties ⋮ Existence and global asymptotic stability of fuzzy cellular neural networks with time delay in the leakage term and unbounded distributed delays ⋮ Stability analysis of fuzzy genetic regulatory networks with various time delays ⋮ EXPONENTIAL STABILITY ANALYSIS OF NEURAL NETWORKS WITH VARIABLE DELAYS ⋮ Convergence for a class of delayed recurrent neural networks without \(M\)-matrix condition

Cites Work

• Global exponential stability of cellular neural networks with variable delays
• A new stability analysis of delayed cellular neural networks
• Global exponential stability for uncertain cellular neural networks with multiple time-varying delays via LMI approach
• Exponential stability analysis of uncertain stochastic neural networks with multiple delays
• New conditions for global exponential stability of cellular neural networks with delays
• Cellular neural networks: theory
• Linear Matrix Inequalities in System and Control Theory
• Input-to-state stability (ISS) analysis for dynamic neural networks
• New conditions for global stability of neural networks with application to linear and quadratic programming problems

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