Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/5717
Title: Evolution of fuzzy system models: An overview and new directions
Authors: Çelikyılmaz, Aslı
Türkşen, İsmail Burhan
Keywords: Data mining
Fuzzy systems
Knowledge discovery
Soft computing
Issue Date: 2007
Publisher: Springer Verlag
Source: 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computer, RSFDGrC 2007, 14 May 2007 through 17 May 2007, Toronto, 71080
Abstract: Fuzzy System Models (FSM), as one of the constituents of soft computing methods, are used for mining implicit or unknown knowledge by approximating systems using fuzzy set theory. The undeniable merit of FSM is its inherent ability of dealing with uncertain, imprecise, and incomplete data and still being able to make powerful inferences. This paper provides an overview of FSM techniques with an emphasis on new approaches on improving the prediction performances of system models. A short introduction to soft computing methods is provided and new improvements in FSMs, namely, Improved Fuzzy Functions (IFF) approaches is reviewed. IFF techniques are an alternate representation and reasoning schema to Fuzzy Rule Base (FRB) approaches. Advantages of the new improvements are discussed. © Springer-Verlag Berlin Heidelberg 2007.
URI: https://doi.org/10.1007/978-3-540-72530-5_14
https://hdl.handle.net/20.500.11851/5717
ISBN: 9783540725299
ISSN: 0302-9743
Appears in Collections:Endüstri Mühendisliği Bölümü / Department of Industrial Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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