| 粒子群最小二乘支持向量机在GPS高程拟合中的应用 |
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| 引用本文： | 黄磊,张书毕,王亮亮,张秋昭.粒子群最小二乘支持向量机在GPS高程拟合中的应用[J].测绘科学,2010,35(5):190-192. |
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| 作者姓名： | 黄磊 张书毕 王亮亮 张秋昭 |
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| 作者单位： | 中国矿业大学环境与测绘学院,江苏徐州,221008;中国矿业大学环境与测绘学院,江苏徐州,221008;江苏省资源环境信息工程重点实验室,江苏徐州,221008;河南理工大学资源环境学院,河南焦作,454000 |
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| 摘 要： | 针对传统的GPS高程拟合方法要求有足够多样本数据的缺陷,本文采用粒子群(PSO)算法优化最小二乘支持向量机(LSSVM)参数的方法进行GPS高程拟合。实验表明,在有限样本的情况下,PSO-LSS-VM模型不仅发挥了LSSVM处理小样本数据的能力,而且通过PSO优化后的LSSVM能够选择出合适的参数;与LM-BP神经网络、标准最小二乘支持向量机等方法比较,PSO-LSSVM模型拟合精度较高。
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| 关 键 词： | 粒子群(PSO)算法 最小二乘支持向量机(LSSVM) 高程异常 GPS高程拟合 |
Application of least squares support vector machines in GPS elevation fitting based on particle swarm optimization |
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| Abstract: | Viewing from the defects that the traditional GPS elevation fitting methods require enough data,the paper put forward that using particle swarm optimization(PSO) algorithm to optimize least squares support vector machines(LSSVM) parameter for GPS elevation fitting.Experiments showed that,in the cases of limited data,not only PSO-LSSVM model could bring into play LSSVM ability to process the small sampled data,but also LSSVM optimized by PSO could choose the appropriate parameters.Comparing with LM-BP neural network and standard least squares support vector machine,PSO-LSSVM model has better fitting accuracy. |
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| Keywords: | particle swarm optimization(PSO) algorithm LSSVM abnormal height GPS elevation fitting |
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