NDLI: Research on forecast of GDP based on process neural network

Content Provider	IEEE Xplore Digital Library
Author	Li Ge Bo Cui
Copyright Year	2011
Description	Author affiliation: School of Computer and Information Engineering, Harbin University of Commerce, 150028, China (Li Ge; Bo Cui)
Abstract	For the multivariate forecast of Gross Domestic Product (GDP), the common features of traditional forecast methods are difficult to express the time cumulative effects in real forecast, and on the other hand, the factors influencing GDP have very typical timing characteristics. Therefore, in consideration of increasing GDP forecast accuracy, process neural network (PNN) was used into the GDP forecast. Making use of the feature of time-varying input function in PNN, the time and space cumulative effect of GDP influence factors was adequately considered into the forecast, and penalty factor was introduced to PNN training to improve BP algorithm. The GDP forecast model of Heilongjiang Province was established based on the above improved algorithm and it was compared and analyzed with the traditional method. The result shows that the PNN model has higher accuracy.
Starting Page	821
Ending Page	824
File Size	272402
Page Count	4
File Format	PDF
ISBN	9781424499502
ISSN	21579563
e-ISBN	9781424499533
DOI	10.1109/ICNC.2011.6022203
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Analytical models artificial neural network process neural network Economic indicators Neurons Predictive models penalty factor Biological neural networks GDP forecast
Content Type	Text
Resource Type	Article

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