| Combining Model for Regional GPS Height Conversion Based on Least Squares Support Vector Machines | |
| Wang Jigang; Hu Yonghui; Zhou Jiangcun | |
| 2009 | |
| 会议名称 | International Conference on Environmental Science and Information Application Technology (ESIAT 2009) |
| 会议录名称 | 2009 INTERNATIONAL CONFERENCE ON ENVIRONMENTAL SCIENCE AND INFORMATION APPLICATION TECHNOLOGY, VOL II, PROCEEDINGS |
| 会议日期 | JUL 04-05, 2009 |
| 会议地点 | Wuhan, PEOPLES R CHINA |
| 产权排序 | 1 |
| ISBN | 978-0-7695-3682-8 |
| 摘要 | In geographical information engineering, height anomalies must be known in order to convert GPS ellipsoid heights into geodetic heights. There are many conversion models, such as polynomial, BP neural network and multi-quadrics fitting. Because the quasi-geoid is an irregular geometric object, every method has both advantages and disadvantages, and is appropriate to different conversion patterns. It is difficult to identify which conversion model is most suitable for a particular area. In order to obtain a more precise and reliable analytical result, the combined model based on least squares support vector machines (LSSVM) has been approached. Derived from statistical learning theory, LSSVM are learning systems that use a hypothesis space of linear function in a high dimensional feature space, trained with a learning algorithm from optimizations theory. As a result, the fitting ability of single models is significantly improved. The combined model still possesses the same important features as the single models. Examples are presented and the results are analyzed in detail to demonstrate the efficiency of the proposed methodology. |
| 部门归属 | 时间用户系统研究室 |
| 主办者 | Intelligent Informat Technol Applicat Res Assoc, Indt Geodesy & Geophys, Chinese Acad Sci, Engn Technol Press |
| 收录类别 | CPCI |
| 语种 | 英语 |
| 文献类型 | 会议论文 |
| 条目标识符 | http://210.72.145.45/handle/361003/5264 |
| 专题 | 时间用户系统研究室 |
| 推荐引用方式 GB/T 7714 | Wang Jigang,Hu Yonghui,Zhou Jiangcun. Combining Model for Regional GPS Height Conversion Based on Least Squares Support Vector Machines[C],2009. |
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