Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7405
Title: Review of fuzzy system models with an emphasis on fuzzy functions
Authors: Türkşen, İsmail Burhan
Keywords: fuzzy clustering
fuzzy functions
fuzzy system models
Type 1 and 2 fuzzy system models
Issue Date: 2009
Publisher: Sage Publications Ltd
Abstract: Fuzzy system modelling (FSM) is one of the most prominent tools that can be used to identify the behaviour of highly non-linear systems with uncertainty. In the past, FSM techniques utilized Type 1 fuzzy sets in order to capture the uncertainty in the system. However, since Type I fuzzy sets express the belongingness of a crisp value x' of an input variable x in a fuzzy set A by a crisp membership value mu(A)(x'), they cannot fully capture the uncertainties associated with higher-order imprecisions in identifying membership functions. In the future, we are likely to observe higher types of fuzzy sets, such as Type 2 fuzzy sets. The use of Type 2 fuzzy sets and linguistic logical connectives has drawn a considerable amount of attention in the realm of FSM in the last two decades. In this paper, we first review Type I fuzzy system models known as Zadeh, Takagi-Sugeno and Turksen models; then we review potentially future realizations of Type 2 fuzzy systems again under the headings of Zadeh, Takagi-Sugeno and Turksen fuzzy system models, in contrast to Type I fuzzy system models. Zadeh's and Takagi-Sugeno's models are essentially fuzzy rule base (FRB) models, whereas Turksen's models are essentially fuzzy function (FF) models. Type 2 fuzzy system models have a higher predictive power. One of the essential problems of Type 2 fuzzy system models is computational complexity. In data-driven FSM methods discussed here, a fuzzy C-means (FCM) clustering algorithm is used in order to identify the system structure, ie, either the number of fuzzy rules or alternately the number of FFs.
URI: https://doi.org/10.1177/0142331208090627
https://hdl.handle.net/20.500.11851/7405
ISSN: 0142-3312
1477-0369
Appears in Collections:Endüstri Mühendisliği Bölümü / Department of Industrial Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Show full item record

CORE Recommender

SCOPUSTM   
Citations

15
checked on Sep 23, 2022

WEB OF SCIENCETM
Citations

13
checked on Sep 24, 2022

Page view(s)

10
checked on Dec 26, 2022

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.