Exponential convergence behavior of solutions to shunting inhibitory cellular neural networks with delays and time-varying coefficients

DOI10.1016/j.mcm.2007.10.007zbMath1145.34368OpenAlexW2052291708MaRDI QIDQ949561

Yaqiong Li, Li-Hong Huang

Publication date: 21 October 2008

Published in: Mathematical and Computer Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.mcm.2007.10.007

zbMATH Keywords

exponential convergencedelaystime-varying coefficientsshunting inhibitory cellular neural networks

Mathematics Subject Classification ID

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

Related Items (7)

Pseudo almost periodic solutions for shunting inhibitory cellular neural networks with continuously distributed delays ⋮ Numerical simulation of periodic solutions for a class of numerical discretization neural networks ⋮ Exponential convergence for high-order recurrent neural networks with a class of general activation functions ⋮ Existence and global exponential stability of almost periodic solutions for SICNNs with nonlinear behaved functions and mixed delays ⋮ Existence and global convergence of periodic solutions in recurrent neural network models with a general piecewise alternately advanced and retarded argument ⋮ Global exponential convergence for delayed cellular neural networks with a class of general activation functions ⋮ On anti-periodic solutions of a shunting inhibitory cellular neural networks with distributed delays

Cites Work

• Global exponential stability of almost periodic solution for a large class of delayed dynamical systems
• Existence and exponential stability of almost periodic solutions for shunting inhibitory cellular neural networks with continuously distributed delays
• Global exponential stability of periodic solution for shunting inhibitory CNNs with delays
• Shunting inhibitory cellular neural networks: derivation and stability analysis

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