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Fuzzy Interval TSK Type‐2 Modeling with Parameterized Conjunctors
Author(s) -
Aras Ayse Cisel,
Kaynak Okyay
Publication year - 2015
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.944
Subject(s) - parameterized complexity , interval (graph theory) , membership function , benchmark (surveying) , fuzzy logic , fuzzy set , type (biology) , fuzzy control system , nonlinear system , mathematics , computer science , artificial intelligence , mathematical optimization , algorithm , ecology , physics , geodesy , combinatorics , quantum mechanics , geography , biology
In this study, a novel approach is described to the design of an interval type‐2 fuzzy neural system (IT2 FNS). It differs from the classical IT2 FNS in its use of parameterized conjunctors. In the optimization of the IT2 FNS, the membership functions are kept fixed and only the parameters of the conjunctors and the parameters in the consequent are tuned. In this study, the gradient based learning algorithm is used. The approach is tested for the modeling of a benchmark nonlinear function and for the wheel slip control of a quarter car model (QCM). In the stated applications, in the absence of any expert knowledge, some knowledge about the system is gained by the use of the interval type‐2 fuzzy c‐means (IT2 FCM) clustering algorithm. Nevertheless, this requires the number of classes to be known beforehand. To alleviate this problem, some validity indices that have been suggested in the literature and a novel validity index that carries less computational burden are considered to determine the number of classes and the number of fuzzy rules. Simulation studies are presented and compared with the results from the literature.