Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/9644
Title: Uncertainty modeling of improved fuzzy functions with evolutionary systems
Authors: Çelik Yılmaz, Aslı
Turksen, I. Burhan
Keywords: type-2 fuzzy functions
uncertainty modeling
Issue Date: 2008
Publisher: IEEE-Inst Electrical Electronics Engineers Inc
Abstract: This paper introduce a type-2 fuzzy function system for uncertainty modeling using evolutionary algorithms (ET2FF). The type-1 fuzzy inference systems (FISs) with fuzzy functions, which do not entail if ... then rule bases, have demonstrated better performance compared to traditional FIS. Nonetheless, the performance of these approaches is usually affected by their uncertain parameters. The proposed method implements a three-phase learning strategy to capture the uncertainties in fuzzy function systems induced by learning parameters, as well as fuzzy function structures. The improved fuzzy clustering initially finds hidden structures, and the genetic learning algorithm optimizes interval type-2 fuzzy sets to capture their optimum uncertainty interval. The proposed ET2FF architecture is evaluated using an extensive suite of real-life applications such as manufacturing process and financial market modeling. The results show that the proposed ET2FF method is comparable-if not superior-to earlier FIS in terms of generalization performance and robustness.
URI: https://doi.org/10.1109/TSMCB.2008.924587
https://hdl.handle.net/20.500.11851/9644
ISSN: 1083-4419
1941-0492
Appears in Collections:PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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