Effects of type reduction algorithms on forecasting accuracy of IT2FLS models Khosravi, Abbas and Nahavandi, Saeid 2014, Effects of type reduction algorithms on forecasting accuracy of IT2FLS models, Applied soft computing journal, vol. 17, pp. 32-38, doi: 10.1016/j.asoc.2013.12.007. Attached Files Name Description MIMEType Size Downloads Title Effects of type reduction algorithms on forecasting accuracy of IT2FLS models Author(s) Khosravi, Abbas orcid.org/0000-0001-6927-0744 Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Journal name Applied soft computing journal Volume number 17 Start page 32 End page 38 Total pages 7 Publisher Elsevier Place of publication Amsterdam, The Netherlands Publication date 2014-04 ISSN 1568-4946 Keyword(s) Bootstrap Delta Electricity price Neural networks Prediction intervals Science & Technology Technology Computer Science, Artificial Intelligence Computer Science, Interdisciplinary Applications Computer Science FUZZY-LOGIC SYSTEMS NEURAL-NETWORK POWER-GENERATION IDENTIFICATION OPTIMIZATION FPGA SETS Summary Type reduction (TR) is one of the key components of interval type-2 fuzzy logic systems (IT2FLSs). Minimizing the computational requirements has been one of the key design criteria for developing TR algorithms. Often researchers give more rewards to computationally less expensive TR algorithms. This paper evaluates and compares five frequently used TR algorithms based on their contribution to the forecasting performance of IT2FLS models. Algorithms are judged based on the generalization power of IT2FLS models developed using them. Synthetic and real world case studies with different levels of uncertainty are considered to examine effects of TR algorithms on forecasts' accuracies. As per obtained results, Coupland-Jonh TR algorithm leads to models with a higher and more stable forecasting performance. However, there is no obvious and consistent relationship between the widths of the type reduced set and the TR algorithm. © 2013 Elsevier B.V. Language eng DOI 10.1016/j.asoc.2013.12.007 Field of Research 080108 Neural, Evolutionary and Fuzzy Computation 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 Persistent URL http://hdl.handle.net/10536/DRO/DU:30069959 Document type: Journal Article Collections: Institute for Intelligent Systems Research and Innovation (IISRI) 2018 ERA Submission GTP Research Related Links Link Description Connect to published version 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 Tue, 24 Feb 2015, 08:37:55 EST Tue, 24 Feb 2015, 08:38:45 EST Tue, 24 Feb 2015, 09:58:51 EST Tue, 24 Feb 2015, 10:29:26 EST Tue, 03 Mar 2015, 05:06:41 EST Thu, 05 Mar 2015, 10:00:49 EST Tue, 17 Mar 2015, 14:29:30 EST Filtered Full Citation counts:   Cited 11 times in TR Web of Science Cited 13 times in Scopus Search Google Scholar Access Statistics: 542 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Tue, 24 Feb 2015, 08:38:45 EST

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