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A network of chaotic elements for information processing

delete1996-01-01
delete66
PRE
AI
I
Ishii, S *
K
Kenji Fukumizu
S
Sumio Watanabe
DOI：10.1016/0893-6080(95)00100-Xdelete
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Abstract

Abstract

En 中文
A globally coupled map (GCM) model is a network of chaotic elements that are globally coupled with each other. In this paper, first, a modified GCM model called the ''globally coupled map using the symmetric map (S-GCM)'' is proposed. The S-GCM is designed for information-processing applications. The S-GCM has attractors called ''cluster frozen attractors'', each of which is taken to represent information. This paper also describes the following characteristics of the S-GCM which are important to information-processing applications: (a) the S-GCM falls into one of the cluster frozen attractors over a wide range of parameters. This means that the information representation is stable over parameters; (b) represented information can be preserved or broken by controlling parameters; (c) the cluster partitioning is restricted, i.e. the representation of information has a limitation. Finally, our techniques for applying the S-GCM to information processing are shown, considering these characteristics. Two associative memory systems are proposed and their performance is compared with that of the Hopfield network.
Keywords:
chaos
nonequilibrium dynamics
globally coupled map
spatiotemporal chaos
chaotic neural network
associative memory

Journal

Neural Networks cover
Neural Networks
IF:
6.3
Papers:
7.8K
Citations:
3.0W

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No organization information available