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. Attached Files Name Description MIMEType Size Downloads Title Approximation of centroid end-points and switch points for replacing type reduction algorithms Author(s) Salaken, Syed Moshfeq 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 Related Links Link Description Connect to published version Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 05 Oct 2015, 14:51:36 EST Tue, 06 Oct 2015, 04:10:20 EST Tue, 06 Oct 2015, 07:39:48 EST Wed, 02 Mar 2016, 08:35:11 EST Mon, 07 Mar 2016, 07:24:36 EST Thu, 10 Mar 2016, 05:23:25 EST Thu, 05 May 2016, 15:02:46 EST Thu, 05 May 2016, 15:03:39 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 125 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Wed, 02 Mar 2016, 08:35:11 EST

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