Hybrid robust approach for TSK fuzzy modeling with outliers

This study proposes a hybrid robust approach for constructing Takagi–Sugeno–Kang (TSK) fuzzy models with outliers. The approach consists of a robust fuzzy C-regression model (RFCRM) clustering algorithm in the coarse-tuning phase and an annealing robust back-propagation (ARBP) learning algorithm in...

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Vydáno v:	Expert systems with applications Ročník 36; číslo 5; s. 8925 - 8931
Hlavní autoři:	Chuang, Chen-Chia, Jeng, Jin-Tsong, Tao, Chin-Wang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.07.2009
Témata:	Hybrid robust approach Outliers Robust clustering algorithm Robust learning algorithm TSK fuzzy model Outliers Robust learning algorithm TSK fuzzy model Hybrid robust approach Robust clustering algorithm
ISSN:	0957-4174, 1873-6793
On-line přístup:	Získat plný text
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Popis
Shrnutí:	This study proposes a hybrid robust approach for constructing Takagi–Sugeno–Kang (TSK) fuzzy models with outliers. The approach consists of a robust fuzzy C-regression model (RFCRM) clustering algorithm in the coarse-tuning phase and an annealing robust back-propagation (ARBP) learning algorithm in the fine-tuning phase. The RFCRM clustering algorithm is modified from the fuzzy C-regression models (FCRM) clustering algorithm by incorporating a robust mechanism and considering input data distribution and robust similarity measure into the FCRM clustering algorithm. Due to the use of robust mechanisms and the consideration of input data distribution, the fuzzy subspaces and the parameters of functions in the consequent parts are simultaneously identified by the proposed RFCRM clustering algorithm and the obtained model will not be significantly affected by outliers. Furthermore, the robust similarity measure is used in the clustering process to reduce the redundant clusters. Consequently, the RFCRM clustering algorithm can generate a better initialization for the TSK fuzzy models in the coarse-tuning phase. Then, an ARBP algorithm is employed to obtain a more precise model in the fine-tuning phase. From our simulation results, it is clearly evident that the proposed robust TSK fuzzy model approach is superior to existing approaches in learning speed and in approximation accuracy.
Bibliografie:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23
ISSN:	0957-4174 1873-6793
DOI:	10.1016/j.eswa.2008.11.053