| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 2576988 | International Congress Series | 2006 | 4 Pages |
Abstract
The constraint satisfaction problem (CSP) is to find a variable-value assignment which satisfies all given constraints. Because of its well-defined representation ability, it can compactly represent many problems in AI. To solve the CSP, we proposed a neural network called LPPH-CSP. In this paper, we extend LPPH-CSP to deal with the CSP which has an objective function and linear inequality constraints.
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Authors
Takahiro Nakano, Masahiro Nagamatu,