A Prediction Model of Peasants’ Income in China Based on BP Neural Network

According to the related data affecting the peasants’ income in China in the years 1978-2008, a total of 13 indices are selected, such as agricultural population, output value of primary industry, and rural employees. According to standardized method and BP neural network method, the peasants’ income and the artificial neural network model are established and analyzed. Results show that the simulation value agrees well with the real value; the neural network model with improved BP algorithm has high prediction accuracy, rapid convergence rate and good generalization ability. Finally, suggestions are put forward to increase the peasants’ income, such as promoting the process of urbanization, developing small and medium-sized enterprises in rural areas, encouraging intensive operation, and strengthening the rural infrastructure and agricultural science and technology input.


Subject(s):
Issue Date:
2011-04
Publication Type:
Journal Article
PURL Identifier:
http://purl.umn.edu/113491
Published in:
Asian Agricultural Research, Volume 03, Issue 04
Page range:
88-94
Total Pages:
4




 Record created 2017-04-01, last modified 2017-11-15

Fulltext:
Download fulltext
PDF

Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)