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

In 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 modu...

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Main Author:	Yap K.S.
Other Authors:	24448864400
Format:	Conference paper
Published:	2023
Subjects:	Bayesian Formalism Multi Agent System Online Sequential Extreme Learning Machine Pattern Classification Decision making E-learning Learning systems Neural networks Sequential machines Empirical studies Multi agent Single-agent Multi agent systems
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id	my.uniten.dspace-30469
record_format	dspace
spelling	my.uniten.dspace-304692023-12-29T15:48:14Z A new multi agent system based on online sequential extreme learning machines and Bayesian Formalism Yap K.S. 24448864400 Bayesian Formalism Multi Agent System Online Sequential Extreme Learning Machine Pattern Classification Decision making E-learning Learning systems Neural networks Sequential machines Bayesian Formalism Empirical studies Multi agent Online Sequential Extreme Learning Machine Single-agent Multi agent systems In 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. Final 2023-12-29T07:48:13Z 2023-12-29T07:48:13Z 2011 Conference paper 10.1109/ICNSC.2011.5874946 2-s2.0-79959990546 https://www.scopus.com/inward/record.uri?eid=2-s2.0-79959990546&doi=10.1109%2fICNSC.2011.5874946&partnerID=40&md5=fa74bfc897f54cf27b4c086ed4102200 https://irepository.uniten.edu.my/handle/123456789/30469 5874946 74 79 Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	Bayesian Formalism Multi Agent System Online Sequential Extreme Learning Machine Pattern Classification Decision making E-learning Learning systems Neural networks Sequential machines Bayesian Formalism Empirical studies Multi agent Online Sequential Extreme Learning Machine Single-agent Multi agent systems
spellingShingle	Bayesian Formalism Multi Agent System Online Sequential Extreme Learning Machine Pattern Classification Decision making E-learning Learning systems Neural networks Sequential machines Bayesian Formalism Empirical studies Multi agent Online Sequential Extreme Learning Machine Single-agent Multi agent systems Yap K.S. A new multi agent system based on online sequential extreme learning machines and Bayesian Formalism
description	In 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.
author2	24448864400
author_facet	24448864400 Yap K.S.
format	Conference paper
author	Yap K.S.
author_sort	Yap K.S.
title	A new multi agent system based on online sequential extreme learning machines and Bayesian Formalism
title_short	A new multi agent system based on online sequential extreme learning machines and Bayesian Formalism
title_full	A new multi agent system based on online sequential extreme learning machines and Bayesian Formalism
title_fullStr	A new multi agent system based on online sequential extreme learning machines and Bayesian Formalism
title_full_unstemmed	A new multi agent system based on online sequential extreme learning machines and Bayesian Formalism
title_sort	new multi agent system based on online sequential extreme learning machines and bayesian formalism
publishDate	2023
_version_	1806426029771718656
score	13.222552

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

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