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Training of interval type-2 fuzzy logic system using extreme learning machine for load forecasting
conference contributionposted 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.
EventUbiquitous Information Management and Communication. International Conference (2015 : Bali, Indonesia)
Pagination1 - 5
PublisherAssociation for Computing Machinery
Place of publicationNew York, N.Y.
Publication classificationE Conference publication; E1 Full written paper - refereed
Copyright notice2015, Association for Computing Machinery
Title of proceedingsACM IMCOM : Proceedings of the 2015 Ubiquitous Information Management and Communication Conference
CategoriesNo categories selected