| 黄土孔隙性分类判别的人工神经网络方法 |
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| 引用本文： | 蔡煜东,宫家文.黄土孔隙性分类判别的人工神经网络方法[J].地质科学,1993,28(4):378-382. |
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| 作者姓名： | 蔡煜东 宫家文 |
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| 作者单位： | 中国科学院上海冶金研究所 上海 200050 |
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| 摘 要： | 运用人工神经网络的一典型模型——“反向传播”神经网络,对洛川黄土孔隙性的实测数据进行了分析,建立了洛川黄土孔隙性预测的计算机智能专家系统。结果表明,神经网络方法性能良好,可望成为黄土孔隙性分类、判别的有效辅助手段。
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| 关 键 词： | 人工神经网络 微孔隙定量研究 洛川黄土 “反向传播”模型 |
| 收稿时间： | 1992-04-01 |
| 修稿时间： | 1992-04-01; |
CLASSIFICATION AND DISCRIMINATION OF LOESS POROSITY BY MAN-MADE NEURAL NETWORK TECHNIQUE |
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| Cai Yudong Gong Jiawen Gan Junren Yao Linsheng.CLASSIFICATION AND DISCRIMINATION OF LOESS POROSITY BY MAN-MADE NEURAL NETWORK TECHNIQUE[J].Chinese Journal of Geology,1993,28(4):378-382. |
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| Authors: | Cai Yudong Gong Jiawen Gan Junren Yao Linsheng |
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| Institution: | Shanghai Institute of Metallurgy Acadcmia Sinica, Shanghai 200050 |
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| Abstract: | The paper applied the back-propagation model which is one of the typical neural networks to analysis of loess porosity. By using the measured data, an intellig-ental expert system to predict luochuan loess porosity was constructed. The results, show that the neural network method is good, and therefore it might be referred as an effective assistant technique for classification and recognition of loess porosity. |
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| Keywords: | Quantitative study of mini hole Luochuan loess porosity Artificial neural networks Back-propagation model |
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