| 基于粗糙集理论与遗传规划的地表下沉系数预测研究 |
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| 引用本文： | 翟淑花,丁桂伶,张亮.基于粗糙集理论与遗传规划的地表下沉系数预测研究[J].城市地质,2015(Z1):37-41. |
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| 作者姓名： | 翟淑花 丁桂伶 张亮 |
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| 作者单位： | 北京市地质研究所,北京,100120 |
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| 摘 要： | 利用粗糙集理论处理大数据量.消除冗余信息等方面的优势,计算出下沉系数各影响因素的属性重要性,约简了遗传规划训练样本集,建立了基于粗糙集和遗传规划的地表下沉系数预测模型。并与BP神经网络法预测结果进行了对比,结果表明,本模型具有精度高,收敛速度快等特点,将其应用到地表下沉系数预测中是可行的。
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| 关 键 词： | 粗糙集 属性约简 遗传规划 下沉系数 |
Subsidence Coefifcient Prediction Based on Rough Set Theory and Genetic Programming |
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| ZHAI Shuhua,DING Guiling,ZHANG Liang.Subsidence Coefifcient Prediction Based on Rough Set Theory and Genetic Programming[J].City Geology,2015(Z1):37-41. |
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| Authors: | ZHAI Shuhua DING Guiling ZHANG Liang |
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| Abstract: | Taking the advantages of rough set theory, such as dealing with large quantities of data and eliminating redundant information, significance of main influencing factors of subsidence coefficient was calculated, training sample data of genetic programming were reduced, and the prediction model of subsidence coefifcient combining rough set and genetic programming is established. The prediction results of the proposed model are compared with those of BP. The ifnal result shows that the proposed model possesses characteristic of high accuracy and rapid convergence, is feasible to be used to predict the subsidence coefifcient. |
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| Keywords: | Rough set Attribution reduction Genetic programming Subsidence coefifcient |
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