| 基于神经网络的农业机械化水平影响因子权重测算和分析 |
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| 引用本文： | 马雁军,李建英,张兆同.基于神经网络的农业机械化水平影响因子权重测算和分析[J].中国制造业信息化,2006,35(3):74-77. |
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| 作者姓名： | 马雁军 李建英 张兆同 |
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| 作者单位： | [1]南京农业大学工学院,江苏南京210031 [2]河北工程大学医学部,河北邯郸056000 |
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| 摘 要： | 为对农业机械化水平影响因子的权重进行测定，建立了相应的BP神经网络模型，并进一步分析了10个因子对农机化水平的影响。研究发现：农机化水平受第一产业人员占全社会从业人员比重的强烈影响，在10个输入因素中所占比重为19．85％；其次是从业人员中初中以上文化程度所占比重、耕播收综合机械化水平。
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| 关 键 词： | 农业机械化水平 BP神经网络 因子权重 |
| 收稿时间： | 2005-12-28 |
The Factors Weight Measurement and Analysis of Agricultural Mechanization Level Affect with BP Neural Network |
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| MA Yan- jun, LI Jian ying, ZHANG Zhao- tong.The Factors Weight Measurement and Analysis of Agricultural Mechanization Level Affect with BP Neural Network[J].Manufacture Information Engineering of China,2006,35(3):74-77. |
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| Authors: | MA Yan- jun LI Jian ying ZHANG Zhao- tong |
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| Affiliation: | 1. Nanjing Agricultural University, Jiangsu Nanjing, 210031, China;2. Hebei University of Engineering, Hebei Handan, 056000, China |
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| Abstract: | To measure some factors weight in agricultural mechanization, it uses BP neural network to simulate and analyze ten factors to determine the agricultural mechanization level. The results show that the method can describe the agricultural mechanization level precisely. Agricultural mechanization level is mainly affected by the employers proportion to the first industry with all the society, accounting up to 19.85 % in weight among the ten factors. The other factors are education level, mechanization level about ploughing, sowing and reaping, net income and planting area. |
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| Keywords: | Agricultural Mechanization Level BP Neural Network Factor Weight |
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