Title :
A fuzzy clustering method for multidimensional parameter selection in system with uncertain parameters
Author :
Kamei, Katsuari ; Auslander, David M. ; Inoue, Kazuo
Author_Institution :
Dept. of Comput. Sci. & Syst. Eng., Ritsumeikan Univ., Kyoto, Japan
Abstract :
The authors present a new multivariate analysis method for multidimensional parameter selection in such problems as controller design for nonlinear systems or other systems for which analytical solutions are not available. The method is based on the fuzzy clustering technique. This new multivariate analysis method is based on the most popular fuzzy clustering algorithm, the fuzzy c-means algorithm (FCM). To apply the FCM method to multivariate binary analysis an effective distance replaces the distance as originally defined in FCM, and a distance weight is defined. Together, they allow the finding of pass regions in the parameter space, and the estimation of the number of pass regions
Keywords :
fuzzy set theory; pattern recognition; statistical analysis; binary analysis; controller design; fuzzy c-means algorithm; fuzzy clustering method; fuzzy set theory; multidimensional parameter selection; multivariate analysis; nonlinear systems; pass regions; pattern recognition; statistical analysis; uncertain parameters; Algorithm design and analysis; Clustering algorithms; Clustering methods; Control systems; Fuzzy systems; Multidimensional systems; Nonlinear control systems; Nonlinear systems; Performance analysis; System performance;
Conference_Titel :
Fuzzy Systems, 1992., IEEE International Conference on
Conference_Location :
San Diego, CA
Print_ISBN :
0-7803-0236-2
DOI :
10.1109/FUZZY.1992.258641
