Open AccessPrediction of Regional Economic Trend with f Improved BP Neural Network Algorithm
Author(s) -
Anmin Liu,
Jiasong Zhu,
Donghai Yue,
Guangxin Liu
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/750/1/012233
Subject(s) - artificial neural network , index (typography) , sample (material) , cobb–douglas production function , investment (military) , econometrics , production (economics) , value (mathematics) , economics , foreign direct investment , computer science , artificial intelligence , microeconomics , macroeconomics , machine learning , chemistry , chromatography , politics , world wide web , political science , law
Traditional BP neural network-based prediction method cannot accurately predictregional economic trend, and the operation efficiency is low. To this end, a method based on improved BP neural network algorithm is developed to predict regional economic trend. The Cobb-Douglas production function is used to select the most influential number of working people and economic investment in fixed assets, fiscal expenditure, gross foreign export value, retail sales of social consumer goods index as the sample data and normalized. The error of sample data is calculated by TangXC_BPModel model, and the the weights are modified uniformly so that and the regional economic trend is predicted accurately.