A hybrid neural network model for noisy data regression

Lee, Eric W.M., Lim, Chee Peng, Yuen, Richard K.K. and Lo, S.M. 2004, A hybrid neural network model for noisy data regression, IEEE Transactions on systems, man, and cybernetics, Part B: Cybernetics, vol. 34, no. 2, pp. 951-960, doi: 10.1109/TSMCB.2003.818440.

Attached Files
Name Description MIMEType Size Downloads

Title A hybrid neural network model for noisy data regression
Author(s) Lee, Eric W.M.
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Yuen, Richard K.K.
Lo, S.M.
Journal name IEEE Transactions on systems, man, and cybernetics, Part B: Cybernetics
Volume number 34
Issue number 2
Start page 951
End page 960
Total pages 10
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, NJ
Publication date 2004-04
ISSN 1083-4419
Keyword(s) Fuzzy adaptive resonance theory (ART)
General regression neural network (GRNN)
General regression neural network with fuzzy ART clustering (GRNNFA)
Noisy data regression
Summary A hybrid neural network model, based on the fusion of fuzzy adaptive resonance theory (FA ART) and the general regression neural network (GRNN), is proposed in this paper. Both FA and the GRNN are incremental learning systems and are very fast in network training. The proposed hybrid model, denoted as GRNNFA, is able to retain these advantages and, at the same time, to reduce the computational requirements in calculating and storing information of the kernels. A clustering version of the GRNN is designed with data compression by FA for noise removal. An adaptive gradient-based kernel width optimization algorithm has also been devised. Convergence of the gradient descent algorithm can be accelerated by the geometric incremental growth of the updating factor. A series of experiments with four benchmark datasets have been conducted to assess and compare effectiveness of GRNNFA with other approaches. The GRNNFA model is also employed in a novel application task for predicting the evacuation time of patrons at typical karaoke centers in Hong Kong in the event of fire. The results positively demonstrate the applicability of GRNNFA in noisy data regression problems.
Language eng
DOI 10.1109/TSMCB.2003.818440
Field of Research 099999 Engineering not elsewhere classified
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1.1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30048092

Document type: Journal Article
Collections: Institute for Frontier Materials
GTP Research
Connect to link resolver
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 58 times in TR Web of Science
Scopus Citation Count Cited 53 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 520 Abstract Views, 1 File Downloads  -  Detailed Statistics
Created: Mon, 03 Sep 2012, 15:29:03 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.