DocumentCode
3025223
Title
Evolutionary strategies for fuzzy models: local vs global construction
Author
Sudkamp, Thomas ; Spiegel, Daniel
Author_Institution
Dept. of Comput. Sci., Wright State Univ., Dayton, OH, USA
fYear
1999
fDate
36342
Firstpage
203
Lastpage
207
Abstract
This paper presents a framework for studying the effectiveness of evolutionary strategies for generating fuzzy rule bases from training data. The fitness measure needed for selection is obtained by a comparison of the training data with the function approximation defined by a fuzzy rule base. The properties of employing both global and local fitness measures are examined. Rule base completion is obtained by incorporating a global evaluation of the smoothness of the transitions between local regions into the selection process
Keywords
function approximation; fuzzy logic; learning (artificial intelligence); pattern clustering; uncertainty handling; clustering techniques; evolutionary strategies; fitness measure; function approximation; fuzzy models; fuzzy rule bases; learning; training data; Algorithm design and analysis; Clustering algorithms; Computer science; Data analysis; Fuzzy sets; Marine vehicles; Quantization; Takagi-Sugeno-Kang model; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Information Processing Society, 1999. NAFIPS. 18th International Conference of the North American
Conference_Location
New York, NY
Print_ISBN
0-7803-5211-4
Type
conf
DOI
10.1109/NAFIPS.1999.781683
Filename
781683
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