Dynamically Weighted Ensemble Neural Networks with Generalized
基于广义回归网络的动态权重回归型神经网络集成方法研究*

Combining the outputs of several neural networks into an aggregate output often gives improved accuracy over any individual output. This paper presents an ensemble method for regression that has advantages over weighted average combining techniques. Generally, the output of an ensemble is a weighted sum which are weights fixed. The ensembles are weighted dynamically, the weights dynamically determined from the predicted accuracies of the trained networks with training dataset, the more accurate a network seems to be of its prediction, the higher the weight. This is implemented by generalized regression neural network. Empirical results show that this method improved on prediction accuracy.