Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/18645
Title: Transformation between type-2 TSK fuzzy systems and an uncertain Gaussian mixture model
Authors: Zhang, Q
Chung, FL 
Wang, S
Keywords: Additive fuzzy models
Gaussian mixture models
TSK models
Type-2 fuzzy systems
Issue Date: 2010
Publisher: Springer
Source: Soft computing, 2010, v. 14, no. 7, p. 701-711 How to cite?
Journal: Soft computing 
Abstract: In this paper, an interval extension of the Gaussian mixture model called uncertain Gaussian mixture model (UGMM) is proposed and its transformation into the additive type-2 TSK fuzzy systems is presented. The conditions under which a UGMM becomes a corresponding type-2 TSK fuzzy system are derived theoretically. Furthermore, the mathematical equivalence between the conditional mean of a UGMM and the defuzzified output of a type-2 TSK fuzzy system is proved. Our results provide a new perspective for type-2 TSK fuzzy systems, i. e., interpreting them from a probabilistic viewpoint. Thus, instead of directly estimating the parameters of the fuzzy rules in a type-2 TSK fuzzy system, we can first estimate the parameters of the corresponding UGMM using any popular density estimation algorithm like the expectation maximization (EM) algorithm. Our experimental results clearly indicate that a type-2 fuzzy system trained in such a new way has higher approximation accuracy and stronger robustness than current type-2 fuzzy systems.
URI: http://hdl.handle.net/10397/18645
ISSN: 1432-7643
DOI: 10.1007/s00500-009-0459-4
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