| 基于粒子群优化的支持向量机在地表沉降预测中的应用 |
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| 引用本文： | 吕福祥,黄磊.基于粒子群优化的支持向量机在地表沉降预测中的应用[J].测绘信息与工程,2010,35(2):44-45. |
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| 作者姓名： | 吕福祥 黄磊 |
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| 作者单位： | [1]山西焦煤集团西山煤电集团公司,太原西矿街345号,030053 [2]中国矿业大学环境与测绘学院,徐州三环南路262号,221116 |
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| 摘 要： | 讨论了利用粒子群优化（PSO）算法来优化选择支持向量机（SVM）参数的原理，分析了三种方法在地表沉降预测中的实例，结果表明PSO-SVM模型预测精度高。
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| 关 键 词： | 粒子群优化 支持向量机 地表沉降 预测 |
APPLICATION OF SUPPORT VECTOR MACHINE WITH PARTICLE SWARM OPTIMIZATION TO EARTH SURFACE SUBSIDENCE PREDICTION |
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| LV Fuxiang,HUANG Lei.APPLICATION OF SUPPORT VECTOR MACHINE WITH PARTICLE SWARM OPTIMIZATION TO EARTH SURFACE SUBSIDENCE PREDICTION[J].Journal of Geomatics,2010,35(2):44-45. |
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| Authors: | LV Fuxiang HUANG Lei |
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| Institution: | 1. Xishan Coal Mines in Shanxi Coking Coal Group Company, 345 Xikuang Road, Taiyuan Shanxi 030053, China; 2 School of Environment Science and Spatial Informatics of CUMT, 2262 Sanhuan South Road, Xuzhou 221116, China) |
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| Abstract: | We discussed the principle of using particle swarm optimization （PSO） to optimize and select the parameters of support vector machine （SVM） ,and the combination of the specific implementation process. Through example, three methods in the application of the earth surface subsidence prediction are analyzed. The results show that PSO-SVM model has better precision and can be applied to the earth surface subsidence prediction. |
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| Keywords: | particle swarm optimization algorithm support vector machine surface subsidence prediction |
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