DocumentCode :
316699
Title :
Continuous optimization schemes for fuzzy classification
Author :
Blekas, K. ; Papageorgiou, G. ; Stafylopatis, A.
Author_Institution :
Dept. of Electr. Eng., Nat. Tech. Univ. of Athens, Greece
Volume :
1
fYear :
1997
fDate :
2-4 Jul 1997
Firstpage :
265
Abstract :
Two approaches are developed, which are suitable for the optimization of a fuzzy classification scheme through the formation of appropriate space-filling clusters. The first approach is based on the analog Hopfield (1985) neural network, while the second one uses real-encoded genetic optimization. Experimental results concerning difficult classification problems show that both proposed approaches are very successful in generating fuzzy partitions and outperform other known algorithms in terms of the correct placement of patterns into partitions
Keywords :
Hopfield neural nets; fuzzy neural nets; genetic algorithms; pattern classification; algorithms; analog Hopfield neural network; classification problem; continuous optimization; experimental results; fuzzy classification; fuzzy partitions; pattern classification; pattern placement; real-encoded genetic optimization; space-filling clusters; Clustering algorithms; Fuzzy sets; Fuzzy systems; Genetic algorithms; Hopfield neural networks; Intelligent systems; Partitioning algorithms; Pattern classification; Pattern clustering; Shape;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Digital Signal Processing Proceedings, 1997. DSP 97., 1997 13th International Conference on
Conference_Location :
Santorini
Print_ISBN :
0-7803-4137-6
Type :
conf
DOI :
10.1109/ICDSP.1997.628057
Filename :
628057
Link To Document :
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