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
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;
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
DOI :
10.1109/NAFIPS.1999.781683
