Application of least squares vector machines in modelling water vapor and carbon dioxide fluxes over a cropland

	Application of least squares vector machines in modelling water vapor and carbon dioxide fluxes over a cropland
	Li Jun; Yu Qiang
	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
内容类型	EI期刊论文
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.