Global exponential stability in Lagrange sense for recurrent neural networks with both time-varying delays and general activation functions via LMI approach

DOI10.1016/j.nonrwa.2010.12.031zbMath1238.34141OpenAlexW1970027711MaRDI QIDQ546172

Zhengwen Tu, Kang Wang, Ji-Gui Jian

Publication date: 24 June 2011

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

Full work available at URL: https://doi.org/10.1016/j.nonrwa.2010.12.031

zbMATH Keywords

neural networkLMI approachglobally exponentially attractive setHalanay delay differential inequalityLagrange exponential stability

Mathematics Subject Classification ID

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

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Cites Work

• Global dissipativity of stochastic neural networks with time delay
• Exponential stability of high-order bidirectional associative memory neural networks with time delays
• A constructive proof on the existence of globally exponentially attractive set and positive invariant set of general Lorenz family
• Multistability and multiperiodicity of delayed Cohen-Grossberg neural networks with a general class of activation functions
• Stability analysis for the generalized Cohen-Grossberg neural networks with inverse Lipschitz neuron activations
• Global exponential stability in Lagrange sense for recurrent neural networks with time delays
• Stability in Lagrange sense for Cohen-Grossberg neural networks with time-varying delays and finite distributed delays
• LMI-based approach for delay-dependent exponential stability analysis of BAM neural networks
• Invariant set and attractivity of nonlinear differential equations with delays
• Stability analysis of Cohen--Grossberg neural network with both time-varying and continuously distributed delays
• Global dissipativity of neural networks with both variable and unbounded delays
• A new approach to exponential stability analysis of neural networks with time-varying delays

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