Fusion of GRNN and FA for Online Noisy Data Regression

Authors:
Richard K. K. Yuen;Eric W. M. Lee;C. P. Lim;Grace W. Y. Cheng
Affiliations:
Fire Safety and Disaster Prevention Research Group, Department of Building and Construction,City University of Hong Kong, Tat Chee Avenue, Kowloon Tong, Hong Kong (SAR), P. R. China;Fire Safety and Disaster Prevention Research Group, Department of Building and Construction,City University of Hong Kong, Tat Chee Avenue, Kowloon Tong, Hong Kong (SAR), P. R. China;School of Electrical and Electronic Engineering, University of Science Malaysia, Penang, Malaysia;Fire Safety and Disaster Prevention Research Group, Department of Building and Construction,City University of Hong Kong, Tat Chee Avenue, Kowloon Tong, Hong Kong (SAR), P. R. China
Venue:
Neural Processing Letters
Year:
2004

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Abstract

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 K of 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.