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.
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;
Conference_Titel :
Adaptive and Learning Systems, 2006 IEEE Mountain Workshop on
Conference_Location :
Logan, UT
Print_ISBN :
1-4244-0166-6
DOI :
10.1109/SMCALS.2006.250720