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Building fuzzy inference systems with similarity reasoning : NSGAII-based fuzzy rule selection and evidential functions

Jee,TL, Chai,KC, Tay,KM and Lim,CP 2014, Building fuzzy inference systems with similarity reasoning : NSGAII-based fuzzy rule selection and evidential functions, in FUZZ-IEEE 2014 : Proceedings of the 2014 IEEE International Conference on Fuzzy Systems, IEEE, Piscataway, N.J., pp. 2192-2197, doi: 10.1109/FUZZ-IEEE.2014.6891738.

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Title Building fuzzy inference systems with similarity reasoning : NSGAII-based fuzzy rule selection and evidential functions
Author(s) Jee,TL
Chai,KC
Tay,KM
Lim,CPORCID iD for Lim,CP orcid.org/0000-0003-4191-9083
Conference name IEEE International Conference on Fuzzy Systems (2014 : Beijing, China)
Conference location Beijing, China
Conference dates 6-11 Jul. 2014
Title of proceedings FUZZ-IEEE 2014 : Proceedings of the 2014 IEEE International Conference on Fuzzy Systems
Editor(s) [Unknown]
Publication date 2014
Conference series IEEE International Conference on Fuzzy Systems
Start page 2192
End page 2197
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) evidential functions
Fuzzy Inference System
fuzzy rule selection
Non-Dominated Sorting Genetic Algorithms-II
Similarity Reasoning
Summary In our previous investigations, two Similarity Reasoning (SR)-based frameworks for tackling real-world problems have been proposed. In both frameworks, SR is used to deduce unknown fuzzy rules based on similarity of the given and unknown fuzzy rules for building a Fuzzy Inference System (FIS). In this paper, we further extend our previous findings by developing (1) a multi-objective evolutionary model for fuzzy rule selection; and (2) an evidential function to facilitate the use of both frameworks. The Non-Dominated Sorting Genetic Algorithms-p (NSGA-p) is adopted for fuzzy rule selection, in accordance with the Pareto optimal criterion. Besides that, two new evidential functions are developed, whereby given fuzzy rules are considered as evidence. Simulated and benchmark examples are included to demonstrate the applicability of these suggestions. Positive results were obtained.
ISBN 9781479920723
ISSN 1098-7584
Language eng
DOI 10.1109/FUZZ-IEEE.2014.6891738
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, Institute of Electrical and Electronics Engineers
Persistent URL http://hdl.handle.net/10536/DRO/DU:30070586

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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