Linear systems in a saturated mode and convergence as gain becomes large of asymptotically stable equilibrium points of neural nets

DOI10.1007/BF01225697zbMath0941.93051OpenAlexW1981981901MaRDI QIDQ1971585

Bruce D. Calvert

Publication date: 24 July 2000

Published in: Circuits, Systems, and Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf01225697

zbMATH Keywords

asymptotic stabilityequilibrianeural netslinear systems in saturation mode

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20) Asymptotic stability in control theory (93D20)

Related Items

Global asymptotic stability for a linear system in a saturated mode ⋮ Necessary and sufficient condition for multistability of neural networks evolving on a closed hypercube

Cites Work

• Asymptotic stability of systems operating on a closed hypercube
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• On the OP‐amp based circuit design of cellular neural networks
• Global convergence of the Hopfield neural network with nonzero diagonal elements
• Minimum-seeking properties of analog neural networks with multilinear objective functions
• Neurons with graded response have collective computational properties like those of two-state neurons.
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