| Registro completo |
| Provedor de dados: |
AgEcon
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| País: |
United States
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| Título: |
A Prediction Model of Peasants’ Income in China Based on BP Neural Network
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| Autores: |
Guo, Qing-chun
He, Zhen-fang
Li, Li
Kong, Ling-jun
Zhang, Xiao-yong
Kou, Li-qun
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| Data: |
2011-08-21
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| Ano: |
2011
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| Palavras-chave: |
BP Neural Network
Peasants’ income
Forecast
China
Agribusiness
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| Resumo: |
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.
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| Tipo: |
Journal Article
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| Idioma: |
Inglês
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| Identificador: |
http://purl.umn.edu/113491
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| Relação: |
Asian Agricultural Research> Volume 03, Issue 04, April 2011
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| Formato: |
4
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