SATURATED OUTPUTS FOR HIGH-GAIN, SELF-EXCITING NETWORKS

DOI10.1142/S0218127491000130zbMath0764.92005OpenAlexW2004458964MaRDI QIDQ4019530

Morris W. Hirsch

Publication date: 16 January 1993

Published in: International Journal of Bifurcation and Chaos (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s0218127491000130

zbMATH Keywords

stable solutionsigmoidsadditive neural networks with symmetric weight matricesasymptotic limiting valuesforced outputshigh-gain self-exciting networksHopfield's saturation theoremnetwork of processing units

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20) Dynamical systems and ergodic theory (37-XX)

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