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.

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Title Effects of type reduction algorithms on forecasting accuracy of IT2FLS models
Author(s) Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Nahavandi, SaeidORCID iD for 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
Electricity price
Neural networks
Prediction intervals
Science & Technology
Computer Science, Artificial Intelligence
Computer Science, Interdisciplinary Applications
Computer Science
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

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