Application of least squares vector machines in modelling water vapor and carbon dioxide fluxes over a cropland
文献类型：EI期刊论文
| 作者 | Li Jun; Yu Qiang |
| 发表日期 | 2005 |
| 关键词 | Carbon dioxide Neural networks Radial basis function networks Vapors |
| 英文摘要 | Least squares support vector machines (LS-SVMs), a nonlinear kernel based machine was introduced to investigate the prospects of application of this approach in modelling water vapor and carbon dioxide fluxes above a summer maize field using the dataset obtained in the North China Plain with eddy covariance technique. The performances of the LS-SVMs were compared to the corresponding models obtained with radial basis function (RBF) neural networks. The results indicated the trained LS-SVMs with a radial basis function kernel had satisfactory performance in modelling surface fluxes; its excellent approximation and generalization property shed new light on the study on complex processes in ecosystem. |
| 出处 | Journal of Zhejiang University: Science
 |
| 卷 | 6 B期:6页:491-495 |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/24652]  |
| 专题 | 地理科学与资源研究所_历年回溯文献 |
| 推荐引用方式 GB/T 7714 | Li Jun,Yu Qiang. Application of least squares vector machines in modelling water vapor and carbon dioxide fluxes over a cropland. 2005. |
入库方式： OAI收割
来源：地理科学与资源研究所
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