An Improved Transiently Chaotic Neural Network with Application to the Maximum Clique Problems
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- Xu Xinshun
- Faculty of Engineering, Toyama University
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- Tang Zheng
- Faculty of Engineering, Toyama University
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- Wang Jiahai
- Faculty of Engineering, Toyama University
Bibliographic Information
- Other Title
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- Improved Transiently Chaotic Neural Network with Application to the Maximum Clique Problems
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Abstract
Abstract By analyzing the dynamic behaviors of the transiently chaotic neural network, we present a improved transiently chaotic neural network(TCNN) model for combinatorial optimization problems and test it on the maximum clique problem. Extensive simulations are performed and the results show that the improved transiently chaotic neural network model can yield satisfactory results on both some graphs of the DIMACS clique instances in the second DIMACS challenge and p-random graphs. It is superior to other algorithms in light of the solution quality and CPU time. Moreover, the improved model uses fewer steps to converge to saturated states in comparison with the original transiently chaotic neural network.
Journal
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- IEEJ Transactions on Electronics, Information and Systems
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IEEJ Transactions on Electronics, Information and Systems 124 (10), 2162-2168, 2004
The Institute of Electrical Engineers of Japan
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Details 詳細情報について
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- CRID
- 1390001204606593152
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- NII Article ID
- 10013641243
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- NII Book ID
- AN10065950
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- ISSN
- 13488155
- 03854221
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- NDL BIB ID
- 7105771
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- Text Lang
- en
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed