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On equivalence of FIS and ELM for interpretable rule-based knowledge representation

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dc.citedby65
dc.contributor.authorWong S.Y.en_US
dc.contributor.authorYap K.S.en_US
dc.contributor.authorYap H.J.en_US
dc.contributor.authorTan S.C.en_US
dc.contributor.authorChang S.W.en_US
dc.contributor.authorid55812054100en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid35319362200en_US
dc.contributor.authorid7403366395en_US
dc.contributor.authorid55276259900en_US
dc.date.accessioned2023-05-29T06:00:11Z
dc.date.available2023-05-29T06:00:11Z
dc.date.issued2015
dc.descriptionClassification (of information); Computer aided diagnosis; Fault detection; Fuzzy systems; Knowledge acquisition; Knowledge representation; Learning systems; Matrix algebra; Membership functions; Pattern recognition; Extreme learning machine; Fault detection and diagnosis; Fuzzy if-then rules; Fuzzy inference systems; Fuzzy membership function; Initialization technique; Interpretable rules; Rule based; Fuzzy inference; algorithm; artificial intelligence; artificial neural network; benchmarking; classification; electric power plant; factual database; feedback system; fuzzy logic; machine learning; nerve cell; reproducibility; statistical model; Algorithms; Artificial Intelligence; Benchmarking; Classification; Databases, Factual; Feedback; Fuzzy Logic; Machine Learning; Models, Statistical; Neural Networks (Computer); Neurons; Power Plants; Reproducibility of Resultsen_US
dc.description.abstractThis paper presents a fuzzy extreme learning machine (F-ELM) that embeds fuzzy membership functions and rules into the hidden layer of extreme learning machine (ELM). Similar to the concept of ELM that employed the random initialization technique, three parameters of F-ELM are randomly assigned. They are the standard deviation of the membership functions, matrix-C (rule-combination matrix), and matrix-D [don't care (DC) matrix]. Fuzzy if-then rules are formulated by the rule-combination Matrix of F-ELM, and a DC approach is adopted to minimize the number of input attributes in the rules. Furthermore, F-ELM utilizes the output weights of the ELM to form the target class and confidence factor for each of the rules. This is to indicate that the corresponding consequent parameters are determined analytically. The operations of F-ELM are equivalent to a fuzzy inference system. Several benchmark data sets and a real world fault detection and diagnosis problem have been used to empirically evaluate the efficacy of the proposed F-ELM in handling pattern classification tasks. The results show that the accuracy rates of F-ELM are comparable (if not superior) to ELM with distinctive ability of providing explicit knowledge in the form of interpretable rule base. � 2012 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo6877713
dc.identifier.doi10.1109/TNNLS.2014.2341655
dc.identifier.epage1430
dc.identifier.issue7
dc.identifier.scopus2-s2.0-84933037890
dc.identifier.spage1417
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84933037890&doi=10.1109%2fTNNLS.2014.2341655&partnerID=40&md5=c9853354cfb08f5b1ff0bef3592487cc
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22319
dc.identifier.volume26
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleIEEE Transactions on Neural Networks and Learning Systems
dc.titleOn equivalence of FIS and ELM for interpretable rule-based knowledge representationen_US
dc.typeArticleen_US
dspace.entity.typePublication
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