An interval type-2 fuzzy logic system-based method for prediction interval construction

Khosravi, Abbas and Nahavandi, Saeid 2014, An interval type-2 fuzzy logic system-based method for prediction interval construction, Applied soft computing journal, vol. 24, pp. 222-231, doi: 10.1016/j.asoc.2014.06.039.

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Title An interval type-2 fuzzy logic system-based method for prediction interval construction
Author(s) Khosravi, AbbasORCID iD for Khosravi, Abbas
Nahavandi, SaeidORCID iD for Nahavandi, Saeid
Journal name Applied soft computing journal
Volume number 24
Start page 222
End page 231
Total pages 10
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2014-11
ISSN 1568-4946
Keyword(s) Interval type 2 fuzzy logic
Prediction interval
Science & Technology
Computer Science, Artificial Intelligence
Computer Science, Interdisciplinary Applications
Computer Science
Summary This paper introduces a new non-parametric method for uncertainty quantification through construction of prediction intervals (PIs). The method takes the left and right end points of the type-reduced set of an interval type-2 fuzzy logic system (IT2FLS) model as the lower and upper bounds of a PI. No assumption is made in regard to the data distribution, behaviour, and patterns when developing intervals. A training method is proposed to link the confidence level (CL) concept of PIs to the intervals generated by IT2FLS models. The new PI-based training algorithm not only ensures that PIs constructed using IT2FLS models satisfy the CL requirements, but also reduces widths of PIs and generates practically informative PIs. Proper adjustment of parameters of IT2FLSs is performed through the minimization of a PI-based objective function. A metaheuristic method is applied for minimization of the non-linear non-differentiable cost function. Performance of the proposed method is examined for seven synthetic and real world benchmark case studies with homogenous and heterogeneous noise. The demonstrated results indicate that the proposed method is capable of generating high quality PIs. Comparative studies also show that the performance of the proposed method is equal to or better than traditional neural network-based methods for construction of PIs in more than 90% of cases. The superiority is more evident for the case of data with a heterogeneous noise. © 2014 Elsevier B.V.
Language eng
DOI 10.1016/j.asoc.2014.06.039
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
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