Staff View: A hybrid ART-GRNN online learning neural network with a ?-insensitive loss function

A hybrid ART-GRNN online learning neural network with a ?-insensitive loss function

In this brief, a new neural network model called generalized adaptive resonance theory (GART) is introduced. GART is a hybrid model that comprises a modified Gaussian adaptive resonance theory (MGA) and the generalized regression neural network (GRNN). It is an enhanced version of the GRNN, which pr...

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Main Authors:	Yap K.S., Lim C.P., Abidin I.Z.
Other Authors:	24448864400
Format:	Article
Published:	2023
Subjects:	Adaptive resonance theory (ART) Bayesian theorem Generalized regression neural network (GRNN) Online sequential extreme learning machine Algorithms Artificial Intelligence Computer Simulation Models, Theoretical Neural Networks (Computer) Online Systems Pattern Recognition, Automated Arts computing E-learning Education Feedforward neural networks Food processing Image classification Internet Learning systems Radial basis function networks Resonance Time series analysis algorithm article artificial intelligence artificial neural network automated pattern recognition computer simulation methodology online system theoretical model Adaptive resonance theories Adaptive resonance theory Empirical studies Extreme Learning Machine Gaussian Generalized regression neural network Hybrid model Loss functions Neural network modelling On-line learning Radial basis function neural networks Sequential learning Time-series prediction Neural networks
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id	my.uniten.dspace-30951
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spelling	my.uniten.dspace-309512023-12-29T15:56:28Z A hybrid ART-GRNN online learning neural network with a ?-insensitive loss function Yap K.S. Lim C.P. Abidin I.Z. 24448864400 55666579300 35606640500 Adaptive resonance theory (ART) Bayesian theorem Generalized regression neural network (GRNN) Online sequential extreme learning machine Algorithms Artificial Intelligence Computer Simulation Models, Theoretical Neural Networks (Computer) Online Systems Pattern Recognition, Automated Arts computing E-learning Education Feedforward neural networks Food processing Image classification Internet Learning systems Radial basis function networks Resonance Time series analysis algorithm article artificial intelligence artificial neural network automated pattern recognition computer simulation methodology online system theoretical model Adaptive resonance theories Adaptive resonance theory Adaptive resonance theory (ART) Bayesian theorem Empirical studies Extreme Learning Machine Gaussian Generalized regression neural network Generalized regression neural network (GRNN) Hybrid model Loss functions Neural network modelling On-line learning Online sequential extreme learning machine Radial basis function neural networks Sequential learning Time-series prediction Neural networks In this brief, a new neural network model called generalized adaptive resonance theory (GART) is introduced. GART is a hybrid model that comprises a modified Gaussian adaptive resonance theory (MGA) and the generalized regression neural network (GRNN). It is an enhanced version of the GRNN, which preserves the online learning properties of adaptive resonance theory (ART). A series of empirical studies to assess the effectiveness of GART in classification, regression, and time series prediction tasks is conducted. The results demonstrate that GART is able to produce good performances as compared with those of other methods, including the online sequential extreme learning machine (OSELM) and sequential learning radial basis function (RBF) neural network models. � 2008 IEEE. Final 2023-12-29T07:56:28Z 2023-12-29T07:56:28Z 2008 Article 10.1109/TNN.2008.2000992 2-s2.0-52149111368 https://www.scopus.com/inward/record.uri?eid=2-s2.0-52149111368&doi=10.1109%2fTNN.2008.2000992&partnerID=40&md5=7f6585894e735f5e16eeedbcc15ed951 https://irepository.uniten.edu.my/handle/123456789/30951 19 9 1641 1646 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	Adaptive resonance theory (ART) Bayesian theorem Generalized regression neural network (GRNN) Online sequential extreme learning machine Algorithms Artificial Intelligence Computer Simulation Models, Theoretical Neural Networks (Computer) Online Systems Pattern Recognition, Automated Arts computing E-learning Education Feedforward neural networks Food processing Image classification Internet Learning systems Radial basis function networks Resonance Time series analysis algorithm article artificial intelligence artificial neural network automated pattern recognition computer simulation methodology online system theoretical model Adaptive resonance theories Adaptive resonance theory Adaptive resonance theory (ART) Bayesian theorem Empirical studies Extreme Learning Machine Gaussian Generalized regression neural network Generalized regression neural network (GRNN) Hybrid model Loss functions Neural network modelling On-line learning Online sequential extreme learning machine Radial basis function neural networks Sequential learning Time-series prediction Neural networks
spellingShingle	Adaptive resonance theory (ART) Bayesian theorem Generalized regression neural network (GRNN) Online sequential extreme learning machine Algorithms Artificial Intelligence Computer Simulation Models, Theoretical Neural Networks (Computer) Online Systems Pattern Recognition, Automated Arts computing E-learning Education Feedforward neural networks Food processing Image classification Internet Learning systems Radial basis function networks Resonance Time series analysis algorithm article artificial intelligence artificial neural network automated pattern recognition computer simulation methodology online system theoretical model Adaptive resonance theories Adaptive resonance theory Adaptive resonance theory (ART) Bayesian theorem Empirical studies Extreme Learning Machine Gaussian Generalized regression neural network Generalized regression neural network (GRNN) Hybrid model Loss functions Neural network modelling On-line learning Online sequential extreme learning machine Radial basis function neural networks Sequential learning Time-series prediction Neural networks Yap K.S. Lim C.P. Abidin I.Z. A hybrid ART-GRNN online learning neural network with a ?-insensitive loss function
description	In this brief, a new neural network model called generalized adaptive resonance theory (GART) is introduced. GART is a hybrid model that comprises a modified Gaussian adaptive resonance theory (MGA) and the generalized regression neural network (GRNN). It is an enhanced version of the GRNN, which preserves the online learning properties of adaptive resonance theory (ART). A series of empirical studies to assess the effectiveness of GART in classification, regression, and time series prediction tasks is conducted. The results demonstrate that GART is able to produce good performances as compared with those of other methods, including the online sequential extreme learning machine (OSELM) and sequential learning radial basis function (RBF) neural network models. � 2008 IEEE.
author2	24448864400
author_facet	24448864400 Yap K.S. Lim C.P. Abidin I.Z.
format	Article
author	Yap K.S. Lim C.P. Abidin I.Z.
author_sort	Yap K.S.
title	A hybrid ART-GRNN online learning neural network with a ?-insensitive loss function
title_short	A hybrid ART-GRNN online learning neural network with a ?-insensitive loss function
title_full	A hybrid ART-GRNN online learning neural network with a ?-insensitive loss function
title_fullStr	A hybrid ART-GRNN online learning neural network with a ?-insensitive loss function
title_full_unstemmed	A hybrid ART-GRNN online learning neural network with a ?-insensitive loss function
title_sort	hybrid art-grnn online learning neural network with a ?-insensitive loss function
publishDate	2023
_version_	1806423361208713216
score	13.222552

A hybrid ART-GRNN online learning neural network with a ?-insensitive loss function

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