New convergence behavior of high-order Hopfield neural networks with time-varying coefficients

DOI10.1016/j.cam.2007.07.011zbMath1161.34050OpenAlexW2070816301MaRDI QIDQ935780

Yuehua Yu, Xuejun Yi, Bing Xiao, Jianying Shao

Publication date: 8 August 2008

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cam.2007.07.011

zbMATH Keywords

neural networksasymptotic theoryretarded differential-difference equations

Mathematics Subject Classification ID

Asymptotic theory of functional-differential equations (34K25) Stability theory of functional-differential equations (34K20)

Related Items (7)

Stationary oscillation for high-order Hopfield neural networks with time delays and impulses ⋮ Global exponential convergence of neutral type competitive neural networks with \(D\) operator and mixed delay ⋮ Exponential convergence for high-order recurrent neural networks with a class of general activation functions ⋮ Convergence for Hopfield neural networks with state-dependent delays ⋮ Existence and global exponential stability of periodic solution for high-order discrete-time BAM neural networks ⋮ Almost periodic functions on time scales and applications ⋮ Existence and exponential stability of periodic solution of high-order Hopfield neural network with delays on time scales

Cites Work

• Exponential stability of high-order bidirectional associative memory neural networks with time delays
• Global exponential stability of impulsive high-order bam neural networks with time-varying delays
• LMI-based criteria for stability of high-order neural networks with time-varying delay
• Novel global stability criteria for high-order Hopfield-type neural networks with time-varying delays
• LMI-based criteria for global robust stability of bidirectional associative memory networks with time delay
• Global asymptotic stability of high-order Hopfield type neural networks with time delays
• Existence and exponential stability of periodic solutions for cellular neural networks with time-varying delays
• Global exponential stability of cellular neural networks with mixed delays and impulses

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