research-article

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

Authors:

Abbas Khosravi,

Jafreezal JaafarAuthors Info & Claims

IMCOM '15: Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication

Article No.: 87, Pages 1 - 5

https://doi.org/10.1145/2701126.2701177

Published: 08 January 2015 Publication History

Abstract

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.

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Cited By

Kayacan ECoupland SJohn RKhanesar M(2017)Elliptic membership functions and the modeling uncertainty in type-2 fuzzy logic systems as applied to time series prediction2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ-IEEE.2017.8015457(1-7)Online publication date: Jul-2017
https://doi.org/10.1109/FUZZ-IEEE.2017.8015457
Hassan SKhosravi AJaafar JKhanesar M(2015)Hybrid Model for the Training of Interval Type-2 Fuzzy Logic SystemNeural Information Processing10.1007/978-3-319-26532-2_71(644-653)Online publication date: 12-Nov-2015
https://doi.org/10.1007/978-3-319-26532-2_71

Index Terms

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

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Published In

cover image ACM Conferences

IMCOM '15: Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication

January 2015

674 pages

ISBN:9781450333771

DOI:10.1145/2701126

Conference Chairs:
Dongsoo S. Kim
Indiana University
,
Sang-wook Kim
Hanyang University, Korea
,
General Chairs:
Suk-Han Lee
Sungkyunkwan Univerisity, Korea
,
Lajos Hanzo
University of Southampton, UK
,
Roslan Ismail
Universiti Kuala Lumpur, Malaysia

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Published: 08 January 2015

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IMCOM '15: The 9th International Conference on Ubiquitous Information Management and Communication

January 8 - 10, 2015

Bali, Indonesia

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Cited By

Kayacan ECoupland SJohn RKhanesar M(2017)Elliptic membership functions and the modeling uncertainty in type-2 fuzzy logic systems as applied to time series prediction2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ-IEEE.2017.8015457(1-7)Online publication date: Jul-2017
https://doi.org/10.1109/FUZZ-IEEE.2017.8015457
Hassan SKhosravi AJaafar JKhanesar M(2015)Hybrid Model for the Training of Interval Type-2 Fuzzy Logic SystemNeural Information Processing10.1007/978-3-319-26532-2_71(644-653)Online publication date: 12-Nov-2015
https://doi.org/10.1007/978-3-319-26532-2_71

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