Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6652
Title: Enhanced type 2 fuzzy system models with improved fuzzy functions
Authors: Çelikyılmaz, Aslı
Türkşen, İsmail Burhan
Keywords: [No Keywords]
Issue Date: 2007
Publisher: IEEE
Source: Annual Meeting of the North-American-Fuzzy-Information-Processing-Society -- JUN 24-27, 2007 -- San Diego, CA
Abstract: A new Fuzzy System Modeling (FSM) approach based on Improved Fuzzy Functions using Discrete Interval Type 2 Fuzzy Sets is presented. The new method is proposed as an alternate learning and reasoning schema to Type 1 and Type 2 FSM with Fuzzy Rule Base (FRB) approaches and enhances Type 2 FSM by reducing complexity and increasing prediction performance. Structure identification of the new approach is based on a supervised Improved Fuzzy Clustering (IFC) method with a dual optimization algorithm, which yields improved membership values. The merit of the proposed Type 2 FSM is that uncertain information on natural grouping of data samples, i.e., membership values, is utilized as additional predictors while structuring fuzzy functions. The uncertainty in selection of the learning parameters are captured by identifying two separate features: executing IFC method with varying levels of fuzziness values, m, and collection of different fuzzy function structures. It is shown with an empirical study that the new Type 2 FSM approach is superior in comparison to earlier Type 1 and Type 2 FSMs in terms of robustness and error reduction.
URI: https://doi.org/10.1109/NAFIPS.2007.383826
https://hdl.handle.net/20.500.11851/6652
ISBN: 978-1-4244-1213-6
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

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