Solution of the optimization problem using the neural network model as a globally coupled map

DOI10.1016/0167-2789(94)90282-8zbMath0860.90102OpenAlexW1977373429MaRDI QIDQ1817036

Hiroshi Nozawa

Publication date: 1 December 1996

Published in: Physica D (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0167-2789(94)90282-8

zbMATH Keywords

traveling salesmanHopfield modelchaotic searchneural network models

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Combinatorial optimization (90C27)

Related Items

Remarks on the mean field dynamics of networks of chaotic elements, Synchronized chaos and other coherent states for two coupled neurons, A fully connected network of Bernoulli units with correlation learning., Chaotic itinerancy, Solving combinatorial optimization problems using augmented Lagrange chaotic simulated annealing, Minimizing interference in satellite communications using transiently chaotic neural networks

Cites Work

• Globally coupled circle maps
• Clustering, coding, switching, hierarchical ordering, and control in a network of chaotic elements
• ``Neural computation of decisions in optimization problems
• On the stability of the travelling salesman problem algorithm of Hopfield and Tank
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• A neural network model as a globally coupled map and applications based on chaos
• Neural networks and physical systems with emergent collective computational abilities.
• Neurons with graded response have collective computational properties like those of two-state neurons.