SVM在阵列信号定位中的应用

Application of Source Location Based on SVM

函数拟合属于基于数据学习的问题。支持向量机是由Vapnik等人提出的小样本统计理论———统计学习理论发展而来的一种新的通用学习算法 ,特别在高维空间中表示复杂函数。该文叙述了基于支持向量机的函数拟合预测信源在水域中的位置 ,并仿真了在不同参数时所收到的不同结果。在小样本情况下 ,采用较大的惩罚值 ,就可以得到较高的精确率 ;同时测试了环境参数确定情况下 ,采用的样本数多也不一定能取得更佳的精确率 ,可见 ,在小样本情况下 。

Data based machine learning includes function regression. Support Vector Machine (SVM) is a novel and powerful learning method which is derived from Statistical Learning Theory (SLT) advanced by Vapnik et al.--a small-sample statistics, especially for representing complex function in high dimensional space. In this paper, a function regression based on SVM is presented to predict source location in ocean environment, and emulated different results by modifying different parameters. On condition of small-sample statistics, using larger penalization value can get higher precision. More samples may not correspond to higher precision when the items of environment parameters are fixed. So the sample number is not the main factor on condition of small-sample statistics.