A neural network model as a globally coupled map and applications based on chaos
- 1 July 1992
- journal article
- Published by AIP Publishing in Chaos: An Interdisciplinary Journal of Nonlinear Science
- Vol. 2 (3), 377-386
- https://doi.org/10.1063/1.165880
Abstract
First, a neural network model as the globally coupled map (GCM) is proposed. The model is obtained by modification of a Hopfield network model that has a negative self‐feedback connection. Second, information processed by this model is interpreted in terms of the variety of the maps acting on the network elements, and a new, dynamic information processing model is described. The search for information using vague keywords, and solution of the traveling salesman problem (TSP) are introduced as applications.Keywords
This publication has 17 references indexed in Scilit:
- Dynamic link of memory—Chaotic memory map in nonequilibrium neural networksNeural Networks, 1992
- Partition complexity in a network of chaotic elementsJournal of Physics A: General Physics, 1991
- Globally coupled chaos violates the law of large numbers but not the central-limit theoremPhysical Review Letters, 1990
- Application of Optical Chaos to Temporal Pattern Search in a Nonlinear Optical ResonatorJapanese Journal of Applied Physics, 1990
- Clustering, coding, switching, hierarchical ordering, and control in a network of chaotic elementsPhysica D: Nonlinear Phenomena, 1990
- Chaotic neural networksPhysics Letters A, 1990
- Harnessing chaos for image synthesisACM SIGGRAPH Computer Graphics, 1988
- Learning representations by back-propagating errorsNature, 1986
- Spatial EEG patterns, non-linear dynamics and perception: the neo-sherringtonian viewBrain Research Reviews, 1985
- Ergodic theory of chaos and strange attractorsReviews of Modern Physics, 1985