Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6482
Title: Decision making with imprecise parameters
Authors: Çelikyılmaz, Aslı
Türkşen, İsmail Burhan
Keywords: Interval-valued membership functions and imprecise functions
Cased-based type reduction
Issue Date: 2010
Publisher: Elsevier Science Inc
Source: Annual Meeting of the North-American-Fuzzy-Information-Processing-Society -- JUN 24-27, 2007 -- San Diego, CA
Abstract: We analyze the impact of imprecise parameters on performance of an uncertainty-modeling tool presented in this paper. In particular, we present a reliable and efficient uncertainty-modeling tool, which enables dynamic capturing of interval-valued clusters representations sets and functions using well-known pattern recognition and machine learning algorithms. We mainly deal with imprecise learning parameters in identifying uncertainty intervals of membership value distributions and imprecise functions. In the experiments, we use the proposed system as a decision support tool for a production line process. Simulation results indicate that in comparison to benchmark methods such as well-known type-1 and type-2 system modeling tools, and statistical machine-learning algorithms, proposed interval-valued imprecise system modeling tool is more robust with less error. (C) 2010 Elsevier Inc. All rights reserved.
URI: https://doi.org/10.1016/j.ijar.2010.06.002
https://hdl.handle.net/20.500.11851/6482
ISSN: 0888-613X
Appears in Collections:Endüstri Mühendisliği Bölümü / Department of Industrial Engineering
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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