Optimization Modeling for GM(1,1) Model Based on BP Neural Network

In grey theory, GM(1,1) model is widely discussed and studied. The purpose of GM(1,1) model is to work on system forecasting with poor, incomplete or uncertain messages. The parameters estimation is an important factor for the GM(1,1) model, thus improving estimation method to enhance the model forecasting accuracy becomes a hot topic of researchers. This study proposes an optimization method for GM(1,1) model based on BP neural network. The GM(1,1) model is mapped to a BP neural network, the corresponding relation between GM(1,1) model parameters and BP network weights is established, the GM(1,1) model parameters estimation problem is transformed into an optimization problem for the weights of neural network. The BP neural network is trained by use of BP algorithm, when the BP network convergence, optimization model parameters can be extracted, and the optimization modeling for GM(1,1) Model based on BP algorithm can be also realized. The experiment results show that the method is feasible and effective, the precision is higher than the traditional method and other optimization modeling methods.