Kanada, Y., 未出版, 1996.
[ English page ]
[ 論文 PDF ファイル ] [ 論文 ポストスクリプト ファイル ]
要旨 (英語のみ): A method for solving large-scale constraint satisfaction problems using an annealed symmetrically-connected neural network, which is called DSN-FAM, is proposed in the present paper. Some conventional methods, such as Hopfield networks, often fail to find a solution. Some others, such as Boltzmann Machines, take too much time. These difficulties are solved by a type of annealing technique, which we call the frustration accumulation method (FAM). DSN-FAM works only with local information, and no global functions or global parameters such as a temperature are used. DSN-FAM thus works autonomously. That is, no external control is necessary while operating. Experiments show that this method does not fail to find a solution and the execution time is less than one tenth of Boltzmann Machines. The performance can be easily and almost linearly improved by parallel processing using tens of processors.
研究テーマ紹介: CCM: 化学的計算のモデル