An Improved Transiently Chaotic Neural Network with Application to the Maximum Clique Problems
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概要
- 論文の詳細を見る
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.
- 社団法人 電気学会の論文
- 2004-10-01
著者
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Tang Zheng
Faculty Of Engineering Miyazaki University
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XU Xinshun
Faculty of Engineering, Toyama University
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WANG Jiahai
the Faculty of Engineering, Toyama University
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Xu Xinshun
Faculty Of Engineering Toyama University
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Wang Jiahai
Faculty Of Engineering Toyama University
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