Solution to China’s GDP Prediction Problem by BP Neural Network

Abstract:

Because the choice and important of learning rate , the higher of η and the faster convergence it will be, but it may cause instability or function vibration if is too high; if is lower, although it may avoid instability, the speed of function convergence will reduce. In order to solve the contradiction, we introduce a variable of , and if the this time is the same as that of the previous time, the weighted summation value will increase and it results in the regulation speed of right value at the stable regulation; and if the this time is contrary to that of the previous time, it indicates that a certain vibration and now the result of summation will make the value of decrease to play a role in stability and increase the speed of function convergence.

• Periodical: Applied Mechanics and Materials (Volumes 50-51)
• Pages: 423-427
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.50-51.423
• Online since: February 2011
• Authors: Juan Li, Ya Feng Ya, Hai Ming Wu
• Keywords: BP Neural Network (BPNN), GDP, Learning Rate, Sigmoid Function

References

[1] Cheng Xin, He Yue. Research on application of GMDH-ARCH combined model in prediction of GDP, Statistics and Consulting(2007).

[2] Zhou Xuan. National GDP prediction model based on time-series analysis, Consume Gui- de·Finance and Accounting Latitude(2009).

[3] Tan Guolan and Li Jian. Application of grey prediction in GDP prediction, Technology & Economy in Areas of Communications(2007).

[4] Ding Webin. Discussion and research on prediction method for total amount of GDP, Statistics and Prediction, Vol, 6: 57-59(2003).

[5] Zhou Zhihua and Cao Cungen. Neural network and it application, Beijing: Tsinghua University Press, (2004).