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An ELM-based single input rule module and its application in power generation

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dc.citedby1
dc.contributor.authorYaw C.T.en_US
dc.contributor.authorWong S.Y.en_US
dc.contributor.authorYap K.S.en_US
dc.contributor.authorid36560884300en_US
dc.contributor.authorid55812054100en_US
dc.contributor.authorid24448864400en_US
dc.date.accessioned2023-05-29T08:10:43Z
dc.date.available2023-05-29T08:10:43Z
dc.date.issued2020
dc.description.abstractExtreme Learning Machine (ELM) is widely known as an effective learning algorithm than the conventional learning methods from the point of learning speed as well as generalization. In traditional fuzzy inference method which was the "if-then" rules, all the input and output objects were assigned to antecedent and consequent component respectively. However, a major dilemma was that the fuzzy rules' number kept increasing until the system and arrangement of the rules became complicated. Therefore, the single input rule modules connected type fuzzy inference (SIRM) method where consociated the output of the fuzzy rules modules significantly. In this paper, we put forward a novel single input rule modules based on extreme learning machine (denoted as SIRM-ELM) for solving data regression problems. In this hybrid model, the concept of SIRM is applied as hidden neurons of ELM and each of them represents a single input fuzzy rules. Hence, the number of fuzzy rule and the number of hidden neuron of ELM are the same. The effectiveness of proposed SIRM-ELM model is verified using sigmoid activation functions based on several benchmark datasets and a NOx emission of power generation plant. Experimental results illustrate that our proposed SIRM-ELM model is capable of achieving small root mean square error, i.e., 0.027448 for prediction of NOx emission. � 2020, Institute of Advanced Engineering and Science. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11591/ijpeds.v11.i1.pp359-366
dc.identifier.epage366
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85090618994
dc.identifier.spage359
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85090618994&doi=10.11591%2fijpeds.v11.i1.pp359-366&partnerID=40&md5=58eab1c248947ad3905c7717484c194d
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25548
dc.identifier.volume11
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleInternational Journal of Power Electronics and Drive Systems
dc.titleAn ELM-based single input rule module and its application in power generationen_US
dc.typeArticleen_US
dspace.entity.typePublication
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