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Approximation of centroid end-points and switch points for replacing type reduction algorithms

Salaken, Syed Moshfeq, Khosravi, Abbas, Nahavandi, Saeid and Wu, Dongrui 2015, Approximation of centroid end-points and switch points for replacing type reduction algorithms, International journal of approximate reasoning, vol. 66, pp. 39-52, doi: 10.1016/j.ijar.2015.07.010.

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Title Approximation of centroid end-points and switch points for replacing type reduction algorithms
Author(s) Salaken, Syed Moshfeq
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Nahavandi, Saeid
Wu, Dongrui
Journal name International journal of approximate reasoning
Volume number 66
Start page 39
End page 52
Total pages 14
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2015-11
ISSN 0888-613X
Keyword(s) Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science
Type-2 fuzzy logic system
Type reduction
FUZZY-LOGIC SYSTEMS
TIME-SERIES
SETS
DEFUZZIFICATION
CONTROLLERS
OPTIMIZATION
PREDICTION
ACCURACY
DESIGN
MODEL
Summary Despite several years of research, type reduction (TR) operation in interval type-2 fuzzy logic system (IT2FLS) cannot perform as fast as a type-1 defuzzifier. In particular, widely used Karnik-Mendel (KM) TR algorithm is computationally much more demanding than alternative TR approaches. In this work, a data driven framework is proposed to quickly, yet accurately, estimate the output of the KM TR algorithm using simple regression models. Comprehensive simulation performed in this study shows that the centroid end-points of KM algorithm can be approximated with a mean absolute percentage error as low as 0.4%. Also, switch point prediction accuracy can be as high as 100%. In conjunction with the fact that simple regression model can be trained with data generated using exhaustive defuzzification method, this work shows the potential of proposed method to provide highly accurate, yet extremely fast, TR approximation method. Speed of the proposed method should theoretically outperform all available TR methods while keeping the uncertainty information intact in the process.
Language eng
DOI 10.1016/j.ijar.2015.07.010
Field of Research 010399 Numerical and Computational Mathematics not elsewhere classified
0103 Numerical And Computational Mathematics
0104 Statistics
0801 Artificial Intelligence And Image Processing
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30078832

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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