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Training of interval type-2 fuzzy logic system using extreme learning machine for load forecasting

Hassan, Saima, Khosravi, Abbas and Jaafar, Jafreezal 2015, Training of interval type-2 fuzzy logic system using extreme learning machine for load forecasting, in ACM IMCOM : Proceedings of the 2015 Ubiquitous Information Management and Communication Conference, Association for Computing Machinery, New York, N.Y., pp. 1-5, doi: 10.1145/2701126.2701177.

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Title Training of interval type-2 fuzzy logic system using extreme learning machine for load forecasting
Author(s) Hassan, Saima
Khosravi, Abbas
Jaafar, Jafreezal
Conference name Ubiquitous Information Management and Communication. International Conference (2015 : Bali, Indonesia)
Conference location Bali, Indonesia
Conference dates 8-10 Jan. 2015
Title of proceedings ACM IMCOM : Proceedings of the 2015 Ubiquitous Information Management and Communication Conference
Publication date 2015
Start page 1
End page 5
Total pages 5
Publisher Association for Computing Machinery
Place of publication New York, N.Y.
Keyword(s) extreme learning machine
interval type-2 fuzzy logic systems
learning algorithm
load forecasting
parameter optimization
Summary Extreme learning machine (ELM) is originally proposed for single- hidden layer feed-forward neural networks (SLFN). From the functional equivalence of fuzzy logic systems and SLFN, the fuzzy logic systems can be interpreted as a special case of SLFN under some mild conditions. Hence the fuzzy logic systems can be trained using SLFN's learning algorithms. Considering the same equivalence, ELM is utilized here to train interval type-2 fuzzy logic systems (IT2FLSs). Based on the working principle of the ELM, the parameters of the antecedent of IT2FLSs are randomly generated while the consequent part of IT2FLSs is optimized using Moore-Penrose generalized inverse of ELM. Application of the developed model to electricity load forecasting is another novelty of the research work. Experimental results shows better forecasting performance of the proposed model over the two frequently used forecasting models.
ISBN 9781450333771
Language eng
DOI 10.1145/2701126.2701177
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 850699 Energy Storage
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, Association for Computing Machinery
Persistent URL http://hdl.handle.net/10536/DRO/DU:30072574

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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