DocumentCode :
2725137
Title :
Fuzzy Modeling Based on Noise Cluster and Possibilistic Clustering
Author :
Ohyama, Isei ; Suzuki, Yukinori ; Saga, Sato ; Maeda, Junji
Author_Institution :
Dept. of Comput. Sci. & Syst. Eng., Muroran Inst. of Technol.
fYear :
2006
fDate :
24-26 July 2006
Firstpage :
225
Lastpage :
230
Abstract :
We propose new fuzzy modeling methods using noise cluster and possibilistic clustering. These modeling methods are based on a switching regression model and a T-S fuzzy model. Since one of the major problems in using a fuzzy clustering algorithm is noise in given data, we employed the noise cluster proposed by Dave to construct a fuzzy model to identify processes of nonlinear plants. Another problem is derived by probabilistic constraint of the FCM algorithm. To solve these problems, we propose a fuzzy model using possibilistic clustering. Fuzzy models using these clustering methods arc proposed in the present paper. Furthermore, computational experiments were carried out to show the effectiveness of the proposed models
Keywords :
fuzzy set theory; pattern clustering; T-S fuzzy model; fuzzy modeling; noise cluster; possibilistic clustering; switching regression model; Clustering algorithms; Clustering methods; Computer science; Covariance matrix; Data engineering; Fuzzy reasoning; Image segmentation; Parameter estimation; Pattern recognition; Systems engineering and theory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Adaptive and Learning Systems, 2006 IEEE Mountain Workshop on
Conference_Location :
Logan, UT
Print_ISBN :
1-4244-0166-6
Type :
conf
DOI :
10.1109/SMCALS.2006.250720
Filename :
4016791
Link To Document :
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