Application of artificial neural networks in tendency forecasting of economic growth

Economic growth results from the synthesis influence of various known or unknown and certain or uncertain factors. Mapping of the stimuli effects and the input and output estimates of artificial neural networks (ANN) are obtained via combinations of nonlinear functions. This approach offers the advantages of self-learning, self-organization, self-adaptation, and fault tolerance, as well as the potential for use in forecasting applications of economic growth. Furthermore, the ANN technology allows the use of multiple variables in both the input and output layers. This capability is very important for economic growth calculations because economic development is often a function of many influential variables. Herein, a forecasting system of economic growth with related application has been proposed, based on ANN. The results show that the Zhejiang proportions of tertiary industry in China for 1995, 1996, and 1997 were 32.305%, 32.174%, and 32.114%, respectively, and the comparative errors eann were 0.64%, − 0.08%, and − 0.27%, respectively, indicating that the forecast result of ANN was better than that of the GM(1.1) model. This method offered better performance and efficiency.