| 基于相空间重构的GA-SVR组合模型边坡位移预测研究 |
| |
| 引用本文： | 刘小生,秦志强.基于相空间重构的GA-SVR组合模型边坡位移预测研究[J].大地测量与地球动力学,2017,37(10):1024-1028. |
| |
| 作者姓名： | 刘小生 秦志强 |
| |
| 摘 要： | 在传统支持向量回归机的基础上，考虑观测数据的混沌特性，通过对训练样本的相空间重构，并结合遗传算法在寻参上的优势，建立边坡变形的相空间重构GA-SVR组合模型。通过组合模型对某矿山边坡位移预测值与实测值进行对比分析，发现组合模型在预测精度上更具优势。
|
| 关 键 词： | 边坡变形 相空间重构 支持向量回归机 位移预测 |
GA-SVR Combined Model for Forecasting Landside Displacement: Study on Based on Phase-Space Reconstruction |
| |
| LIU Xiaosheng,QIN Zhiqiang.GA-SVR Combined Model for Forecasting Landside Displacement: Study on Based on Phase-Space Reconstruction[J].Journal of Geodesy and Geodynamics,2017,37(10):1024-1028. |
| |
| Authors: | LIU Xiaosheng QIN Zhiqiang |
| |
| Abstract: | On the basis of the traditional support vector regression machine, considering the chaotic properties of observation data, the GA-SVR combination model is built by combining the reconstruction of a phase space of training sample and the advantages of a genetic algorithm in seeking the optimum parameter. After comparing and analyzing the predicted and measured values of slope deformation, we determine that the combination model has higher prediction accuracy. |
| |
| Keywords: | LIU Xiaosheng QIN Zhiqiang |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《大地测量与地球动力学》浏览原始摘要信息 |
| 点击此处可从《大地测量与地球动力学》下载免费的PDF全文 |
