Application of Improved BP Neural Network to Predict Agricultural Commodity Total Production Value

摘要

An improved method was proposed in order to accelerate the convergence speed and reduce the training time of back propagation (BP) neural network. The principal component analysis (PCA) was used as the pre-processing to select principal components from the input variables. The regression and correlation analysis were used as the post-processing to analyze the result and test the precision of training. The predicting result of agricultural commodity total production value showed that the training efficiency could be improved and the structure of network could be simplified by the improved BP neural network. The high precision and low error below 2% indicate that this method can be applied to resolve the predicting problem with many variables.