| 农业机械化发展水平的人工神经网络评价模型 |
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| 引用本文： | 楼文高,王延政. 农业机械化发展水平的人工神经网络评价模型[J]. 农业机械学报, 2003, 34(3): 58-61 |
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| 作者姓名： | 楼文高 王延政 |
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| 作者单位： | 1. 上海出版印刷高等专科学校,200093,上海市
2. 上海水产大学机电工程系,200090,上海市 |
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| 基金项目： | 上海水产大学校长专项基金资助项目 (项目编号 :SFU2 0 0 10 5 ) |
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| 摘 要： | 根据农业机械化发展水平的评价标准，提出了生成足够多人工神经网络训练样本、检验样本和测试样本的新方法，给出了区分农业机械化发展水平不同程度的分界值，并提出了确定合理BP神经网络结构的原则。通过上述方法得到的神经网络模型具有更好的泛化能力，且不受网络初始权值的影响。运用训练后的神经网络评价模型对河南省1994年农业机械化发展水平的评价结果表明：与灰色-概率评估模型相比，本文建立的BP评价模型具有更好的客观性、通用性、实用性和容错性。
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| 关 键 词： | 农业机械化 发展水平 人工神经网络 网络评价模型 |
| 修稿时间： | 2002-02-27 |
Comprehensive Assessment Model for Agricultural Mechanization Development Level by Artificial Neural Network |
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| Lou Wen''''gao. Comprehensive Assessment Model for Agricultural Mechanization Development Level by Artificial Neural Network[J]. Transactions of the Chinese Society for Agricultural Machinery, 2003, 34(3): 58-61 |
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| Authors: | Lou Wen''''gao |
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| Affiliation: | Lou Wen'gao(Shanghai Publishing and Printing College) Wang Yanzheng(Shanghai Fisheries University) |
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| Abstract: | A new approach to generate training, verifying and testing was developed. According to the assessment standard of agricultural mechanization development level (AMDL) in this paper, the principle of determining the number of hidden layers and their neurons on each layer of BP neural networks (BPNN) was also discussed. The trained BPNN based model presented in this paper possesses a capacity of higher generalization and is not impacted by the initial values of connection weights. The assessment results of AMDL of He'nan province shows that the new established BPNN based model is objective, reliable, and fault tolerant compared with other methods. |
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| Keywords: | Agricultural mechanization Artificial neural networks Assessment Standard Model |
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