You are not logged in.
DRO

# 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.

Attached Files

Title A Hybrid ART-GRNN Online Learning Neural Network With a $\varepsilon$ -Insensitive Loss Function Yap, Keem Siah Lim, Chee Peng orcid.org/0000-0003-4191-9083 Abidi, Izham Zainal IEEE transactions on neural networks 19 9 1641 1646 6 IEEE Piscataway, N.J. 2008-09 1045-9227 1941-0093 Adaptive resonance theory (ART) Bayesian theorem generalized regression neural network (GRNN) online sequential extreme learning machine 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. eng 10.1109/TNN.2008.2000992 MD Multidisciplinary 099999 Engineering not elsewhere classified 0 Not Applicable C1.1 Refereed article in a scholarly journal ©2008, IEEE http://hdl.handle.net/10536/DRO/DU:30096914

 Document type: Journal Article Centre for Intelligent Systems Research
 Versions Version Filter Type Thu, 25 May 2017, 18:54:07 EST Fri, 26 May 2017, 05:00:34 EST Thu, 08 Jun 2017, 14:49:56 EST Thu, 08 Jun 2017, 14:51:38 EST Wed, 14 Jun 2017, 15:26:34 EST Filtered Full
Citation counts: Cited 21 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar 16 Abstract Views, 0 File Downloads  -  Detailed Statistics Thu, 25 May 2017, 18:54:07 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.