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

conference contribution
posted on 2015-01-01, 00:00 authored by S Hassan, Abbas KhosraviAbbas Khosravi, J Jaafar
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.

History

Event

Ubiquitous Information Management and Communication. International Conference (2015 : Bali, Indonesia)

Pagination

1 - 5

Publisher

Association for Computing Machinery

Location

Bali, Indonesia

Place of publication

New York, N.Y.

Start date

2015-01-08

End date

2015-01-10

ISBN-13

9781450333771

Language

eng

Publication classification

E Conference publication; E1 Full written paper - refereed

Copyright notice

2015, Association for Computing Machinery

Title of proceedings

ACM IMCOM : Proceedings of the 2015 Ubiquitous Information Management and Communication Conference