Controlling Attention to Clauses for Solving SAT by Lagrange Programming Neural Network

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概要

• 論文の詳細を見る

• The satisfiability problem(SAT)of the propositional calculus is one of the most basic and important problems in the computer science. On the other hand the SAT is a famous NP-complete problem. It requires a lot of time to solve in general as the problem size becomes large. We proposed a recurrent analog neural network called LPPH for the SAT. The basic dynamics of the LPPH have the following properties: 1)every equilibrium point of the dynamics is a solution of the analog version of the SAT, called CONSAT, and vice versa, and 2)almost all equilibrium points have some kind of asymptotical stability. Experimental results show that the LPPH can solve the SAT effectively even if it is executed by numerical simulations on conventional computers. In this paper we introduce coefficients called "coefficients of attention" to the dynamics the LPPH and show that the performance of the LPPH for solving the SAT is improved.

• バイオメディカル・ファジィ・システム学会の論文

著者

• Okumura Masahiro

  Asa Systems Inc.

• MIYAMOTO Kazunori

  Department of Neurosurgery, Nara Medical University

• AMAN Hirohisa

  Department of Computer Science, Faculty of Engineering, Ehime University

• NAGAMATU Masahiro

  Department of Electricity, Faculty of Engineering, Kyushu Institute of Technology

• AKAHOSHI Tuyoshi

  Department of Electricity, Faculty of Engineering, Kyushu Institute of Technology

• Akahoshi Tuyoshi

  Department Of Electricity Faculty Of Engineering Kyushu Institute Of Technology

• Miyamoto Kazunori

  Department Of Electricity Faculty Of Engineering Kyushu Institute Of Technology

• Miyamoto Kazunori

  Department Of Clinical Laboratory Tsuchiura Kyodo Hospital

• Aman Hirohisa

  Department Of Electricity Faculty Of Engineering Kyushu Institute Of Technology

• Aman Hirohisa

  Department Of Computer Science Ehime University

• Nagamatu Masahiro

  Department Of Electricity Faculty Of Engineering Kyushu Institute Of Technology

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