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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 | Citation: | 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 |
Appears in Collections: | Staff Publications |
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