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
書誌事項
- タイトル別名
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- Improved Transiently Chaotic Neural Network with Application to the Maximum Clique Problems
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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.
収録刊行物
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- 電気学会論文誌C(電子・情報・システム部門誌)
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電気学会論文誌C(電子・情報・システム部門誌) 124 (10), 2162-2168, 2004
一般社団法人 電気学会
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詳細情報 詳細情報について
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- CRID
- 1390001204606593152
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- NII論文ID
- 10013641243
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- NII書誌ID
- AN10065950
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- ISSN
- 13488155
- 03854221
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- NDL書誌ID
- 7105771
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
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- CiNii Articles
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- 使用不可