New necessary and sufficient conditions for absolute stability of neural networks

DOI10.1016/j.neunet.2006.06.003zbMath1158.68443OpenAlexW2161026336WikidataQ51150969 ScholiaQ51150969MaRDI QIDQ866740

Tianguang Chu, Zhang, Cishen

Publication date: 14 February 2007

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2006.06.003

zbMATH Keywords

absolute stabilityneural networksglobal asymptotic stabilityexponential convergenceasymmetric connectionsolvable Lie algebra condition

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (8)

Peak-to-peak exponential direct learning of continuous-time recurrent neural network models: a matrix inequality approach ⋮ Passive learning and input-to-state stability of switched Hopfield neural networks with time-delay ⋮ Some new results on stability of Takagi-Sugeno fuzzy Hopfield neural networks ⋮ A new robust training law for dynamic neural networks with external disturbance: an LMI approach ⋮ Robust stability of recurrent neural networks with ISS learning algorithm ⋮ Exponential \(\mathcal H_{\infty}\) stable learning method for Takagi-Sugeno fuzzy delayed neural networks: a convex optimization approach ⋮ An equivalent condition for stability properties of Lotka-Volterra systems ⋮ An \(\mathcal H_{\infty}\) approach to stability analysis of switched Hopfield neural networks with time-delay

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