Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.engappai.2005.12.011
Title: Genetic learning and performance evaluation of interval type-2 fuzzy logic controllers
Authors: Wu, Dongrui
Wan Tan, Woei 
Keywords: Genetic algorithms
Modelling uncertainty
Process control
Type-2 fuzzy logic controller
Issue Date: Dec-2006
Source: Wu, Dongrui, Wan Tan, Woei (2006-12). Genetic learning and performance evaluation of interval type-2 fuzzy logic controllers. Engineering Applications of Artificial Intelligence 19 (8) : 829-841. ScholarBank@NUS Repository. https://doi.org/10.1016/j.engappai.2005.12.011
Abstract: Type-2 fuzzy sets, which are characterized by membership functions (MFs) that are themselves fuzzy, have been attracting interest. This paper focuses on advancing the understanding of interval type-2 fuzzy logic controllers (FLCs). First, a type-2 FLC is evolved using Genetic Algorithms (GAs). The type-2 FLC is then compared with another three GA evolved type-1 FLCs that have different design parameters. The objective is to examine the amount by which the extra degrees of freedom provided by antecedent type-2 fuzzy sets is able to improve the control performance. Experimental results show that better control can be achieved using a type-2 FLC with fewer fuzzy sets/rules so one benefit of type-2 FLC is a lower trade-off between modeling accuracy and interpretability. © 2006 Elsevier Ltd. All rights reserved.
Source Title: Engineering Applications of Artificial Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/56128
ISSN: 09521976
DOI: 10.1016/j.engappai.2005.12.011
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