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Extreme Learning Machine neural networks for multi-agent system in power generation

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dc.citedby2
dc.contributor.authorYaw C.T.en_US
dc.contributor.authorWong S.Y.en_US
dc.contributor.authorYap K.S.en_US
dc.contributor.authorTan C.H.en_US
dc.contributor.authorid36560884300en_US
dc.contributor.authorid55812054100en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid55175180600en_US
dc.date.accessioned2023-05-29T06:54:38Z
dc.date.available2023-05-29T06:54:38Z
dc.date.issued2018
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. The hidden neurons are optional in neuron alike whereas the weights are the criteria required to study the linking among the output layer as well as hidden layers. On the other hand, the ensemble model to integrate every independent prediction of several ELMs to produce a final output. This particular approach was included in a Multi-Agent System (MAS). By hybrid those two approached, a novel extreme learning machine based multi-agent systems (ELM-MAS) for handling classification problems is presented in this paper. It contains two layers of ELMs, i.e., individual agent layer and parent agent layer. Several activation functions using benchmark datasets and real-world applications, i.e., satellite image, image segmentation, fault diagnosis in power generation (including circulating water systems as well as GAST governor) were used to test the ELM-MAS developed. Our experimental results suggest that ELM-MAS is capable of achieving good accuracy rates relative to others approaches. � 2018 Authors.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.14419/ijet.v7i4.35.22760
dc.identifier.epage353
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85059238173
dc.identifier.spage347
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85059238173&doi=10.14419%2fijet.v7i4.35.22760&partnerID=40&md5=16e6ae27919ba9648964489d95e3322c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24036
dc.identifier.volume7
dc.publisherScience Publishing Corporation Incen_US
dc.sourceScopus
dc.sourcetitleInternational Journal of Engineering and Technology(UAE)
dc.titleExtreme Learning Machine neural networks for multi-agent system in power generationen_US
dc.typeArticleen_US
dspace.entity.typePublication
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