DocumentCode :
1277488
Title :
Solving constraint satisfaction and optimization problems by a neuro-fuzzy approach
Author :
Cavalieri, Salvatore ; Russo, Marco
Author_Institution :
Ist. di Inf. e Telecommun., Catania Univ., Italy
Volume :
29
Issue :
6
fYear :
1999
fDate :
12/1/1999 12:00:00 AM
Firstpage :
895
Lastpage :
902
Abstract :
The solution of constrained satisfaction and constrained optimization problems using a Hopfield model requires determination of the values of a certain number of coefficients linked to the surrounding conditions of the problem. It is quite difficult to determine these values, mainly because a heuristic search is necessary. This is not only time-consuming but may lead to solutions that are far from optimal, or even nonvalid ones. So far, there have been no works in literature offering a general method for the search for coefficents with will guarantee optimal or close to optimal solutions. This paper proposes a fuzzy approach which allows automatic determination of Hopfield coefficients
Keywords :
Hopfield neural nets; constraint theory; fuzzy logic; optimisation; Hopfield coefficients; Hopfield model; coefficients; constrained optimization problems; constraint satisfaction problems; heuristic search; neuro-fuzzy approach; Computer science; Constraint optimization; Cost function; Engineering management; Fuzzy logic; Fuzzy sets; Fuzzy systems; Hopfield neural networks; Hybrid intelligent systems; Neural networks;
fLanguage :
English
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
Publisher :
ieee
ISSN :
1083-4419
Type :
jour
DOI :
10.1109/3477.809042
Filename :
809042
Link To Document :
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