Paper Info
Title
Rules Extraction of Interval Type-2 Fuzzy Logic System Based on Fuzzy c-Means Clustering
Abstract
An improved clustering algorithm is proposed in this paper, which originates from Fuzzy c-Means Clustering(FCM). FCM is one of the algorithms used commonly to extract fuzzy rules from type-1 fuzzy logic system. However, its application is merely limited to dots set. This deficiency is improved in the new algorithm, Interval Fuzzy c-Means Clustering(IFCM), which is adequate to deal with interval sets. The enhanced algorithm is based on a new definition of distance between interval data. This article will also focus on extracting fuzzy rule from interval type-2 fuzzy systems. The type-2 fuzzy system is suitable to handle the situations with complicated uncertainties. However, how to extract fuzzy rules from type-2 fuzzy logic systems remains an important issue. This paper will attempt to exhibit an unique method to extract rule from interval type-2 fuzzy systems with IFCM. Simulation results are included at the end of this article that indicates the validity of IFCM.
Year
DOI
Venue
2007
10.1109/FSKD.2007.503
FSKD (2)
Keywords
DocType
Volume
type-1 fuzzy logic system,fuzzy set theory,fuzzy reasoning,pattern clustering,fuzzy rule,interval set,interval type-2 fuzzy logic system,fuzzy c-means clustering,type-2 fuzzy system,fuzzy rule extraction,fuzzy logic reasoning,interval type-2 fuzzy logic,enhanced algorithm,fuzzy logic,improved clustering algorithm,type-2 fuzzy logic system,interval data,knowledge acquisition,uncertainty handling,interval fuzzy c-means clustering,rules extraction,new algorithm,fuzzy system
Conference
2
ISBN
Citations 
PageRank 
978-0-7695-2874-8
3
0.44
References 
Authors
6
3
Name
Order
Citations
PageRank
Weibin Zhang13110.03
Huai-zhong Hu230.44
Wenjiang Liu3112.32