Residue Amending Combined BP Prediction Model

The thesis introduces grey system model and BP neural network. Through making full use of the merits of GM(1.1) and neural network model and overcoming their drawbacks, we construct the grey residue amending combined and prediction model based on BP Neural network, and such combined model as "combined prediction model= tendency prediction model/GM(1.1)+neural network model", and makes a contrast between the three models in prediction and precision. The result indicates that, the combined model is better than that of the single models for higher precision and smaller error.