Improved Algorithm Based On Bp Neural Network In Hunan Province Gdp Forecast Study

GDP(Gross Domestic Product) is one of the most important factors in measuring the whole economic situation and status of one country or district. In order to implement a better control or adjustment on the macro-economy, we should firstly make an efficient prediction or forecast of the future economy. On the basis of the predicted result, the decision-maker of government can constitute some plan or project to restrain or stimulate the economy growth. In the current methods, the most common statistics methods are time-series and regression predictions. And macro-economy is a nonlinear system, which keeps changing. Besides, additional interference factors have direct effects on the operation of macro-economy systems, greatly influencing the prediction results. Since the historic date needed for macro-economy models are not stable, not accurate and not complete, it is necessary to solve such problems by using the traditional prediction methods. Therefore, ANN(Artificial Neural Networks) are applied to prediction.Artificial Neural Network is a nonlinear, non-local, non-stationary complex network system. It has parallel distribution and adaptive structure of the brain as an information processing model. And it can complete specific tasks by 'self-learning' or 'training' to learn a large numbers of knowledge. Economic forecasting model using artificial neural network has high accuracy.Many practical examples show that the artificial neural network is very effective method to construct forecasting model. It can learn knowledge from the sample data without complicated inquiry and expression; automatically obtain the functions behind the sample data. It is superior to the traditional time series forecasting methods obviously, especially when the model has strong complexity nonlinear.This paper studies the GDP forecast using the theory of artificial neural networks. We employ a three feed-forward back-propagation neural network to construct forecasting model for GDP forecast using the GDP data of Hunan. When training the networks, we normalize input and output data for the same magnitude. And the predictions are compared with the traditional model; the results show that the GDP forecast using BP model is more available.