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Linear approximation of Karnik-Mendel type reduction algorithm

Salaken, Syed Moshfeq, Khosravi, Abbas, Nahavandi, Saeid and Wu, Dongrui 2015, Linear approximation of Karnik-Mendel type reduction algorithm, 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 Linear approximation of Karnik-Mendel type reduction algorithm
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
FUZZY-LOGIC CONTROLLER
INTERVAL TYPE-2
SYSTEMS
SET
DEFUZZIFICATION
UNCERTAINTY
Summary Karnik-Mendel (KM) algorithm is the most used and researched type reduction (TR) algorithm in literature. This algorithm is iterative in nature and despite consistent long term effort, no general closed form formula has been found to replace this computationally expensive algorithm. In this research work, we demonstrate that the outcome of KM algorithm can be approximated by simple linear regression techniques. Since most of the applications will have a fixed range of inputs with small scale variations, it is possible to handle those complexities in design phase and build a fuzzy logic system (FLS) with low run time computational burden. This objective can be well served by the application of regression techniques. This work presents an overview of feasibility of regression techniques for design of data-driven type reducers while keeping the uncertainty bound in FLS intact. Simulation results demonstrates the approximation error is less than 2%. Thus our work preserve the essence of Karnik-Mendel algorithm and serves the requirement of low
computational complexities.
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:30083083

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