LS-SVM算法中优化训练样本对测深异常值剔除的影响

在验证趋势面滤波是最小二乘支持向量机算法取特定参数解的基础上,利用 LS-SVM 所构造的海底趋势面对测深异常值进行剔除。 为了克服 LS-SVM 解非稀疏性的缺点,同时抑制偏差较大的训练样本对海底趋势面构造的影响,提出并实现了一种基于局部样本中心距离的训练样本优化方法。 为了检验该算法的有效性,选取实测的多波束测深数据进行验证,结果表明在训练样本优化的基础上,通过调整 LS-SVM 的参数可以得到更为合理的海底趋势面,测深异常值地剔除也更为有效。

The Influence of Optimized Train Samples on Elimination of Sounding Outliers in the LS-SVM Arithmetic

After validating the trend filter is the special result to the LS-SVM arithmetic,eliminating the sounding outliers by the seafloor surface which constructed by LS-SVM . In order to solve the sparseness of LS- SVM results meanwhile restrain the influence of the sample-outliers. A new method of optimize samples by part samples center distance is presented. Some practical multi-beam data is chose to verify the correctness and rationality of the new method. The example shows that on the ground of the optimized train samples, the reasonable seafloor surface could be constructed by LS-SVM arithmetic,and then the outliers of multi-beam data could be eliminated effectively.