Publication:
A hybrid fuzzy logic and extreme learning machine for improving efficiency of circulating water systems in power generation plant

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dc.citedby8
dc.contributor.authorAziz N.L.A.A.en_US
dc.contributor.authorYap K.S.en_US
dc.contributor.authorBunyamin M.A.en_US
dc.contributor.authorid55812399400en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid55812855600en_US
dc.date.accessioned2023-12-29T07:45:46Z
dc.date.available2023-12-29T07:45:46Z
dc.date.issued2013
dc.description.abstractThis paper presents a new approach of the fault detection for improving efficiency of circulating water system (CWS) in a power generation plant using a hybrid Fuzzy Logic System (FLS) and Extreme Learning Machine (ELM) neural network. The FLS is a mathematical tool for calculating the uncertainties where precision and significance are applied in the real world. It is based on natural language which has the ability of �computing the word�. The ELM is an extremely fast learning algorithm for neural network that can completed the training cycle in a very short time. By combining the FLS and ELM, new hybrid model, i.e., FLS-ELM is developed. The applicability of this proposed hybrid model is validated in fault detection in CWS which may help to improve overall efficiency of power generation plant, hence, consuming less natural recourses and producing less pollutions. � Published under licence by IOP Publishing Ltd.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo12102
dc.identifier.doi10.1088/1755-1315/16/1/012102
dc.identifier.issue1
dc.identifier.scopus2-s2.0-84881106611
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84881106611&doi=10.1088%2f1755-1315%2f16%2f1%2f012102&partnerID=40&md5=3aad5ca69d775ca08be5246ccc54376b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/30237
dc.identifier.volume16
dc.publisherInstitute of Physics Publishingen_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleIOP Conference Series: Earth and Environmental Science
dc.subjectFault detection
dc.subjectFuzzy logic
dc.subjectLearning systems
dc.subjectWaterworks
dc.subjectCirculating water system
dc.subjectExtreme learning machine
dc.subjectFuzzy logic system
dc.subjectImproving efficiency
dc.subjectMathematical tools
dc.subjectNatural languages
dc.subjectOverall efficiency
dc.subjectPower generation plants
dc.subjectalgorithm
dc.subjectelectricity generation
dc.subjectenergy efficiency
dc.subjectfuzzy mathematics
dc.subjectnatural resource
dc.subjectnumerical model
dc.subjectpollution monitoring
dc.subjectuncertainty analysis
dc.subjectKnowledge acquisition
dc.titleA hybrid fuzzy logic and extreme learning machine for improving efficiency of circulating water systems in power generation planten_US
dc.typeConference paperen_US
dspace.entity.typePublication
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