Switch point finding using polynomial regression for fuzzy type reduction algorithms

Salaken, Syed Moshfeq, Khosravi, Abbas, Nahavandi, Saeid and Wu, Dongrui 2015, Switch point finding using polynomial regression for fuzzy type reduction algorithms, in FUZZ-IEEE 2015: Proceedings of the IEEE International Conference on Fuzzy Systems, IEEE, Piscataway, N.J., pp. 1-6.

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Title Switch point finding using polynomial regression for fuzzy type reduction algorithms
Author(s) Salaken, Syed Moshfeq
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Wu, Dongrui
Conference name IEEE International Conference on Fuzzy Systems (2015 : Istanbul, Turkey)
Conference location Istanbul, Turkey
Conference dates 2-5 Aug. 2015
Title of proceedings FUZZ-IEEE 2015: Proceedings of the IEEE International Conference on Fuzzy Systems
Editor(s) Yazici, A.
Pal, N. R.
Kaymak, U.
Martin, T.
Ishibuchi, H.
Lin, C. T.
Sousa, J. M. C.
Tutmez, B.
Publication date 2015
Series IEEE International Fuzzy Systems Conference Proceedings
Start page 1
End page 6
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Science & Technology
Technology
Engineering, Electrical & Electronic
Engineering
KARNIK-MENDEL ALGORITHMS
INTERVAL TYPE-2
LOGIC SYSTEMS
CONTROLLER
SET
DEFUZZIFICATION
UNCERTAINTY
DESIGN
Summary Karnik-Mendel (KM) algorithm is the most widely used type reduction (TR) method in literature for the design of interval type-2 fuzzy logic systems (IT2FLS). Its iterative nature for finding left and right switch points is its Achilles heel. Despite a decade of research, none of the alternative TR methods offer uncertainty measures equivalent to KM algorithm. This paper takes a data-driven approach to tackle the computational burden of this algorithm while keeping its key features. We propose a regression method to approximate left and right switch points found by KM algorithm. Approximator only uses the firing intervals, rnles centroids, and FLS strnctural features as inputs. Once training is done, it can precisely approximate the left and right switch points through basic vector multiplications. Comprehensive simulation results demonstrate that the approximation accuracy for a wide variety of FLSs is 100%. Flexibility, ease of implementation, and speed are other features of the proposed method.
ISBN 9781467374286
ISSN 1544-5615
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083085

Document type: Conference Paper
Collections: Centre for Intelligent Systems Research
2018 ERA Submission
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