Title :
Compact fuzzy models through complexity reduction and evolutionary optimization
Author :
Roubos, Hans ; Setnes, Magne
Author_Institution :
Control Lab., Delft Univ. of Technol., Netherlands
Abstract :
Genetic algorithms (GAs) and other evolutionary optimization methods to design fuzzy rules from data for systems modeling and classification have received much attention in recent literature. We show that different tools for modeling and complexity reduction can be favorably combined in a scheme with GA-based parameter optimization. Fuzzy clustering, rule reduction, rule base simplification and constrained genetic optimization are integrated in a data-driven modeling scheme with low human intervention. Attractive models with respect to compactness, transparency and accuracy, are the result of this symbiosis
Keywords :
computational complexity; fuzzy set theory; fuzzy systems; genetic algorithms; knowledge based systems; pattern recognition; complexity reduction; data-driven modeling; evolutionary optimization; fuzzy clustering; fuzzy models; fuzzy set theory; genetic algorithms; Algorithm design and analysis; Control engineering; Design methodology; Fuzzy control; Fuzzy sets; Fuzzy systems; Information technology; Laboratories; Modeling; Optical wavelength conversion;
Conference_Titel :
Fuzzy Systems, 2000. FUZZ IEEE 2000. The Ninth IEEE International Conference on
Conference_Location :
San Antonio, TX
Print_ISBN :
0-7803-5877-5
DOI :
10.1109/FUZZY.2000.839128