๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

A neural network approach to the 3-satisfiability problem

โœ Scribed by James L. Johnson


Publisher
Elsevier Science
Year
1989
Tongue
English
Weight
251 KB
Volume
6
Category
Article
ISSN
0743-7315

No coin nor oath required. For personal study only.


๐Ÿ“œ SIMILAR VOLUMES


A neural network approach to the classif
โœ James W. Denton; Ming S. Hung; Barbara A. Osyk ๐Ÿ“‚ Article ๐Ÿ“… 1990 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 612 KB

The task of classifying observations into known groups is a common problem in decision making. A wealth of statistical approaches, commencing with Fisher's linear discriminant function, and including variations to accommodate a variety of modeling assumptions, have been proposed. In addition, nonpar

Neural network approach to the ECM probl
โœ Keniti Gonoi; Takakazu Kurokawa ๐Ÿ“‚ Article ๐Ÿ“… 2000 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 128 KB ๐Ÿ‘ 2 views
A neural network approach to tiling prob
โœ T. Tambouratzis ๐Ÿ“‚ Article ๐Ÿ“… 2001 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 123 KB

The family of tiling problems comprises combinatorial optimization problems involving a grid and a number of shapes. Appropriate placements of the shapes on the grid are sought such that specific constraints concerning shape overlap and grid coverage are satisfied. The family of tiling problems has

A neural network approach to multiobject
โœ Hsu-Shih Shih; Ue-Pyng Wen; S. Lee; Kuen-Ming Lan; Han-Chyi Hsiao ๐Ÿ“‚ Article ๐Ÿ“… 2004 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 781 KB

This study aims at utilizing the dynamic behavior of artificial neural networks (ANNs) to solve multiobjective programming (MOP) and multilevel programming (MLP) problems. The traditional and nontraditional approaches to the MLP are first classified into five categories. Then, based on the approach

Neural network approach to the solution
โœ S. Osowski ๐Ÿ“‚ Article ๐Ÿ“… 1995 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 808 KB

## Abstract The paper presents the application of nonlinear neural optimization networks to solve the linear complementarity problem. Two different approaches are presented and investigated: one leading to linear and the second to quadratic optimization programming. The numerical results of illustr