Fusion of GRNN and FA for online noisy data regression

Yuen, Richard K. K., Lee, Eric W. M., Lim, C.P. and Cheng, Grace W. Y. 2004, Fusion of GRNN and FA for online noisy data regression, Neural processing letters, vol. 19, no. 3, pp. 227-241.

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Title Fusion of GRNN and FA for online noisy data regression
Author(s) Yuen, Richard K. K.
Lee, Eric W. M.
Lim, C.P.
Cheng, Grace W. Y.
Journal name Neural processing letters
Volume number 19
Issue number 3
Start page 227
End page 241
Total pages 15
Publisher Springer
Place of publication Secaucus, United States
Publication date 2004-06
ISSN 1370-4621
1573-773X
Keyword(s) Fuzzy ART
GRNN
GRNNFA
K-nearest-neighbors
Noisy data
Online regression
Summary A new online neural-network-based regression model for noisy data is proposed in this paper. It is a hybrid system combining the Fuzzy ART (FA) and General Regression Neural Network (GRNN) models. Both the FA and GRNN models are fast incremental learning systems. The proposed hybrid model, denoted as GRNNFA-online, retains the online learning properties of both models. The kernel centers of the GRNN are obtained by compressing the training samples using the FA model. The width of each kernel is then estimated by the K-nearest-neighbors (kNN) method. A heuristic is proposed to tune the value of Kof the kNN dynamically based on the concept of gradient-descent. The performance of the GRNNFA-online model was evaluated using two benchmark datasets, i.e., OZONE and Friedman#1. The experimental results demonstrated the convergence of the prediction errors. Bootstrapping was employed to assess the performance statistically. The final prediction errors are analyzed and compared with those from other systems.Bootstrapping was employed to assess the performance statistically. The final prediction errors are analyzed and compared with those from other systems.
Language eng
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
Copyright notice ©2004, Kluwer Academic Publishers
Persistent URL http://hdl.handle.net/10536/DRO/DU:30048771

Document type: Journal Article
Collection: Institute for Frontier Materials
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