Dynamical Analysis of Continuous Higher-Order Hopfield Networks for Combinatorial Optimization

DOI10.1162/0899766054026620zbMath1102.68110OpenAlexW2128299841WikidataQ51971022 ScholiaQ51971022MaRDI QIDQ5696507

F. Sandoval, Miguel Atencia, Gonzalo Joya

Publication date: 18 October 2005

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

Full work available at URL: https://doi.org/10.1162/0899766054026620

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Combinatorial optimization (90C27) Neural networks for/in biological studies, artificial life and related topics (92B20) Stability of solutions to ordinary differential equations (34D20)

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Cites Work

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• Neural Networks for Combinatorial Optimization: A Review of More Than a Decade of Research
• SOME RESULTS ON CHUA'S EQUATION NEAR A TRIPLE-ZERO LINEAR DEGENERACY
• Global convergence of the Hopfield neural network with nonzero diagonal elements
• Minimum-seeking properties of analog neural networks with multilinear objective functions
• One-step and multistep procedures for constrained minimization problems
• Neurons with graded response have collective computational properties like those of two-state neurons.
• Hopfield neural networks for optimization: Study of the different dynamics

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