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. Attached Files Name Description MIMEType Size Downloads Title Switch point finding using polynomial regression for fuzzy type reduction algorithms Author(s) Salaken, Syed Moshfeq orcid.org/0000-0001-8632-2665 Khosravi, Abbas orcid.org/0000-0001-6927-0744 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: Institute for Intelligent Systems Research and Innovation (IISRI) 2018 ERA Submission GTP Research Related Links Link Description   Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 27 Apr 2016, 10:28:52 EST Wed, 27 Apr 2016, 10:34:14 EST Thu, 28 Apr 2016, 05:01:01 EST Thu, 28 Apr 2016, 06:10:42 EST Thu, 19 May 2016, 13:54:02 EST Thu, 19 May 2016, 13:55:29 EST Wed, 09 Nov 2016, 11:45:35 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 3 times in Scopus Search Google Scholar Access Statistics: 591 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Wed, 27 Apr 2016, 10:34:14 EST

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