Prediction interval construction using interval type-2 fuzzy logic systems

Khosravi, Abbas, Nahavandi, Saeid, Creighton, Doug and Naghavizadeh, Reihaneh 2012, Prediction interval construction using interval type-2 fuzzy logic systems, in FUZZ-IEEE/WCCI 2012 : Proceedings of the IEEE 2012 International Conference on Fuzzy Systems, IEEE Computer Society, Los Alamitos, Calif., pp. 1504-1510.

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Title Prediction interval construction using interval type-2 fuzzy logic systems
Author(s) Khosravi, AbbasORCID iD for Khosravi, Abbas
Nahavandi, SaeidORCID iD for Nahavandi, Saeid
Creighton, DougORCID iD for Creighton, Doug
Naghavizadeh, Reihaneh
Conference name International Conference on Fuzzy Systems (2012 : Brisbane, Qld.)
Conference location Brisbane, Qld
Conference dates 10-15 Jun. 2012
Title of proceedings FUZZ-IEEE/WCCI 2012 : Proceedings of the IEEE 2012 International Conference on Fuzzy Systems
Editor(s) [Unknown]
Publication date 2012
Conference series International Conference on Fuzzy Systems
Start page 1504
End page 1510
Total pages 7
Publisher IEEE Computer Society
Place of publication Los Alamitos, Calif.
Keyword(s) confidence level
prediction intervals
type-2 fuzzy logic
Summary This study proposes a novel non-parametric method for construction of prediction intervals (PIs) using interval type-2 Takagi-Sugeno-Kang fuzzy logic systems (IT2 TSK FLSs). The key idea in the proposed method is to treat the left and right end points of the type-reduced set as the lower and upper bounds of a PI. This allows us to construct PIs without making any special assumption about the data distribution. A new training algorithm is developed to satisfy conditions imposed by the associated confidence level on PIs. Proper adjustment of premise and consequent parameters of IT2 TSK FLSs is performed through the minimization of a PI-based objective function, rather than traditional error-based cost functions. This new cost function covers both validity and informativeness aspects of PIs. A metaheuristic method is applied for minimization of the non-linear non-differentiable cost function. Quantitative measures are applied for assessing the quality of PIs constructed using IT2 TSK FLSs. The demonstrated results for four benchmark case studies with homogenous and heterogeneous noise clearly show the proposed method is capable of generating high quality PIs useful for decision-making.
ISBN 9781467315050
ISSN 1098-7584
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
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