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Critical temperature of the transiently chaotic neural network

✍ Scribed by Zhen Ding; Henry Leung; Zhiwen Zhu

Publisher
Elsevier Science
Year
2003
Tongue
English
Weight
326 KB
Volume
37
Category
Article
ISSN
0895-7177

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✦ Synopsis

The dynamical behaviour of an optimizing neural network is closely related to its parameters. For the transiently chaotic neural network (TCNN), the temperature, i.e., self-feedback weighting, is an important parameter for the network performance.

While a high temperature is required to investigate chaotic dynamics, a low temperature is preferred for combinatorial optimization application.

In this article, we derived this critical temperature of the TCNN analytically and illustrated its validity using computer simulation.

πŸ“œ SIMILAR VOLUMES

A study of the transiently chaotic neura
A study of the transiently chaotic neural network for combinatorial optimization
✍ Zhen Ding; Henry Leung; Zhiwen Zhu πŸ“‚ Article πŸ“… 2002 πŸ› Elsevier Science 🌐 English βš– 749 KB

consider the dynamic behavior of the transiently chaotic neural network (TCNN). Although its dynamical behavior is of interest in investigating neural dynamics, we observed that the chaotic phase in a TCNN is not a necessary condition for the network to reach the global solution for a combinatorial

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✍ Hongjie Yu; Yanzhu Liu; Jianhua Peng πŸ“‚ Article πŸ“… 2004 πŸ› Elsevier Science 🌐 English βš– 336 KB

The control chaos method based on contraction mappings is applied to small discrete neural networks for stabilizing a desired periodic orbit embedded in the chaotic attractor by a external input. The suggested method utilizes only an approximate location of the desired periodic orbit, one of the mer