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Enhancement of neural network based multi agent system for classification and regression in energy system

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dc.citedby4
dc.contributor.authorYaw C.T.en_US
dc.contributor.authorYap K.S.en_US
dc.contributor.authorWong S.Y.en_US
dc.contributor.authorYap H.J.en_US
dc.contributor.authorPaw J.K.S.en_US
dc.contributor.authorid36560884300en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid55812054100en_US
dc.contributor.authorid35319362200en_US
dc.contributor.authorid57224774999en_US
dc.date.accessioned2023-05-29T08:11:49Z
dc.date.available2023-05-29T08:11:49Z
dc.date.issued2020
dc.descriptionElectric circuit breakers; Fuzzy inference; Hybrid systems; Iterative methods; Knowledge acquisition; Learning systems; Multi agent systems; Support vector machines; Benchmark datasets; Circulating water system; Extreme learning machine; ITS applications; Network structures; Power generation systems; Trust management; Trust measurement; Neural networksen_US
dc.description.abstractExtreme Learning Machine improved the iterative procedures of adjusting weights by randomly selecting hidden neurons besides analytically determining the output weights. In this paper, the basic ELM neural network was enhanced with a simplified network structure to achieve regression performance. Next, to solve the pattern classification, a hybrid system was proposed which integrated the ELM neural network and MAS models. A MAS model is then designed with a novel trust measurement method to combine ELM neural networks. Firstly, ELM hybrid with Single Input Rule Module (SIRM-ELM) was designed. There was only a single input connected to the rules, where the rules were the hidden neurons of ELM and each represented a single input fuzzy rules. Results showed that the SIRM-ELM model was better than Support Vector Machine and traditional ELM. Secondly, an extreme learning machine based multi agent systems (ELM-MAS) was designed to improve ELM's capability. Its first layer was made up of at least one ELM where ELM acted as an individual agent, whereas another layer was made up of a single ELM acting as the parent agent. Lastly, Certified Belief in Strength (CBS) method was applied to the ELM neural network to form ELM-MAS-CBS, using the reputation and strength of individual agents as the trust measurement. The assembly of strong elements related to the ELM agents formed the trust management that allowed the improvement of the performance in MAS using the CBS method. Both of the developed models were evaluated on its application on the power generation system. The test accuracy rate of both models for circulating water systems was shown to be comparable to other algorithms. In short, the developed models had been verified using benchmark datasets and applied in power generation, where the results were satisfactory. � 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ACCESS.2020.3012983
dc.identifier.epage163043
dc.identifier.scopus2-s2.0-85102884215
dc.identifier.spage163026
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85102884215&doi=10.1109%2fACCESS.2020.3012983&partnerID=40&md5=5b965220af6090132740d4b87709cff9
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25622
dc.identifier.volume8
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleIEEE Access
dc.titleEnhancement of neural network based multi agent system for classification and regression in energy systemen_US
dc.typeArticleen_US
dspace.entity.typePublication
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