NDLI: Adaptive fuzzy regression clustering algorithm for TSK fuzzy modeling

Content Provider	IEEE Xplore Digital Library
Author	Chen-Chia Chuang Chih-Ching Hsiao Jin-Tsong Jeng
Copyright Year	2003
Description	Author affiliation: Dept. of Electron. Eng., Hwa-Hsia Coll. of Technol. & Commerce, Taipei, Taiwan (Chen-Chia Chuang)
Abstract	The TSK type of fuzzy models has attracted a great attention of the fuzzy modeling community due to their good performance in various applications. Some approaches for modeling TSK fuzzy rules have been proposed in the literature. Most of them define their fuzzy subspaces bases based on the idea of training data being close enough instead of having similar functions. In addition, the fuzzy C-regression model (FCRM) clustering algorithm is proposed to construct TSK fuzzy models. However, this approach does not take into account the data distribution. In this paper, a novel TSK fuzzy modeling approach is presented. In this approach, adaptive fuzzy regression clustering (AFRC) algorithm is proposed to simultaneously define fuzzy subspaces and find the parameters in the consequent parts of TSK rules. In addition, the similarity measure is used to reduce the redundant rules in the clustering process. To obtain a more precise model, a gradient descent algorithm is employed. From the simulation results, the proposed TSK fuzzy model approach indeed showed superior performance.
Sponsorship	IEEE Robotics & Automation Soc
Starting Page	201
Ending Page	206
File Size	356958
Page Count	6
File Format	PDF
ISBN	0780378660
DOI	10.1109/CIRA.2003.1222089
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-07-16
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Training data Cities and towns Fuzzy systems Educational institutions Business Computer science Application software Tellurium Supervised learning
Content Type	Text
Resource Type	Article

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