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A new multi agent system based on online sequential extreme learning machines and Bayesian Formalism

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dc.citedby2
dc.contributor.authorYap K.S.en_US
dc.contributor.authorid24448864400en_US
dc.date.accessioned2023-12-29T07:48:13Z
dc.date.available2023-12-29T07:48:13Z
dc.date.issued2011
dc.description.abstractIn this article, a new Multi Agent System based on Online Sequential Extreme Learning Machine (OSELM) neural network and Bayesian Formalism (MAS-OSELM-BF) is introduced. It is an improvement of a single OSELM (single agent) by combined multiple OSELMs (multi agents) with a final decision making module (parent agent). Here, the development of the parent agent is motivated by the Bayesian Formalism. A series of empirical studies to assess the effectiveness of the MAS-OSELM-BF in pattern classification tasks is conducted. The results demonstrated that the MAS-OSELM-BF able to produce good performance as compared with a single OSELM and other method that employed ensemble OSLEM (EOSELM). � 2011 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo5874946
dc.identifier.doi10.1109/ICNSC.2011.5874946
dc.identifier.epage79
dc.identifier.scopus2-s2.0-79959990546
dc.identifier.spage74
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-79959990546&doi=10.1109%2fICNSC.2011.5874946&partnerID=40&md5=fa74bfc897f54cf27b4c086ed4102200
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/30469
dc.pagecount5
dc.sourceScopus
dc.sourcetitle2011 International Conference on Networking, Sensing and Control, ICNSC 2011
dc.subjectBayesian Formalism
dc.subjectMulti Agent System
dc.subjectOnline Sequential Extreme Learning Machine
dc.subjectPattern Classification
dc.subjectDecision making
dc.subjectE-learning
dc.subjectLearning systems
dc.subjectNeural networks
dc.subjectSequential machines
dc.subjectBayesian Formalism
dc.subjectEmpirical studies
dc.subjectMulti agent
dc.subjectOnline Sequential Extreme Learning Machine
dc.subjectSingle-agent
dc.subjectMulti agent systems
dc.titleA new multi agent system based on online sequential extreme learning machines and Bayesian Formalismen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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