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.
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