The Short-Range Precipitation Forecasting Method of Neural Network Based on Principal Component Analysis

The goal of this paper is to study the factors that affect the generalization capability and real-time learning for neural network. First, this paper investigates the effect of initial weight ranges, learning rate, and regularization co-efficient on generalization capability and learning speed. Based on this, this paper proposes a hybrid method that simultaneously considers these three factors, and dynamically tunes the learning rate and regularization coefficient. Then the paper presents the results of some experimental comparison among these kinds of methods in several different problems. Finally, it draws conclusions and makes plan for future work.