Training of interval type-2 fuzzy logic system using extreme learning machine for load forecasting
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...
| Main Authors: | Hassan, S., Khosravi, A., Jaafar, J. |
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| Format: | Conference or Workshop Item |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.26298 / |
| Published: |
Association for Computing Machinery, Inc
2015
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| Online Access: |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84926211661&doi=10.1145%2f2701126.2701177&partnerID=40&md5=c4ec501f2cd7ab601d98ecd11e5a8fb0 http://eprints.utp.edu.my/26298/ |
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| 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. |
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