Title :
A Fuzzy Classifier with Adaptive Learning of Norm Inducing Matrix
Author :
Yang, Tze ; Yao, Leehter
Author_Institution :
Nat. Taipei Univ. of Technol., Taipei
Abstract :
A fuzzy classifier with adaptive learning of the volume of norm inducing matrix is proposed in this paper. The proposed fuzzy classifier improves the Gustafson-Kessel (GK) algorithm which assumes a fixed volume of the norm inducing matrix. An efficient approach based on gradient descent learning is proposed to recursively update the volume of norm inducing matrix. Mathematical analyses and computer simulations are made to show the effectiveness and efficiency of the proposed fuzzy classifier.
Keywords :
fuzzy set theory; learning (artificial intelligence); matrix algebra; pattern classification; Gustafson-Kessel algorithm; adaptive learning; fuzzy classifier; gradient descent learning; norm inducing matrix; Adaptive control; Background noise; Clustering algorithms; Covariance matrix; Ellipsoids; Fuzzy control; Fuzzy sets; Pattern recognition; Programmable control; Prototypes; decision region; fuzzy c means; fuzzy classifier; pattern recognition;
Conference_Titel :
Networking, Sensing and Control, 2007 IEEE International Conference on
Conference_Location :
London
Print_ISBN :
1-4244-1076-2
Electronic_ISBN :
1-4244-1076-2
DOI :
10.1109/ICNSC.2007.372806
