An Improved Transiently Chaotic Neural Network with Application to the Maximum Clique Problems

• Xu Xinshun

  Faculty of Engineering, Toyama University

• Tang Zheng

  Faculty of Engineering, Toyama University

• Wang Jiahai

  Faculty of Engineering, Toyama University

書誌事項

タイトル別名

• Improved Transiently Chaotic Neural Network with Application to the Maximum Clique Problems

抄録

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

• 電気学会論文誌Ｃ（電子・情報・システム部門誌）

  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

キーワード

• Transiently chaotic neural network
• Hopfield neural network
• Maximum clique
• NP-hard problem

詳細情報

• CRID: 1390001204606593152
• NII論文ID: 10013641243
• NII書誌ID: AN10065950
• DOI: 10.1541/ieejeiss.124.2162
• ISSN: 13488155
• ISSN: 03854221
• NDL書誌ID: 7105771
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I7105771
• Web Site: http://www.jstage.jst.go.jp/article/ieejeiss/124/10/124_10_2162/_pdf
• 本文言語コード: en
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