A Hybrid ART-GRNN Online Learning Neural Network With a <formula formulatype="inline"><tex Notation="TeX">$\varepsilon$</tex> </formula>-Insensitive Loss Function

Yap, Keem Siah, Lim, Chee Peng and Abidi, Izham Zainal 2008, A Hybrid ART-GRNN Online Learning Neural Network With a $\varepsilon$ -Insensitive Loss Function, IEEE transactions on neural networks, vol. 19, no. 9, pp. 1641-1646, doi: 10.1109/TNN.2008.2000992.

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Title A Hybrid ART-GRNN Online Learning Neural Network With a $\varepsilon$ -Insensitive Loss Function
Author(s) Yap, Keem Siah
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Abidi, Izham Zainal
Journal name IEEE transactions on neural networks
Volume number 19
Issue number 9
Start page 1641
End page 1646
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2008-09
ISSN 1045-9227
Keyword(s) Adaptive resonance theory (ART)
Bayesian theorem
generalized regression neural network (GRNN)
online sequential extreme learning machine
Summary 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.
Language eng
DOI 10.1109/TNN.2008.2000992
Field of Research MD Multidisciplinary
099999 Engineering not elsewhere classified
Socio Economic Objective 0 Not Applicable
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2008, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30096914

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