针对常用的回归算法由于回归模型不够稀疏而导致的在线检测速度慢的问题,提出基于相关向量机(RVM)回归的惯性约束聚变(ICF)实验靶姿态初步估计。实验中,利用主成分分析法(PCA)提取ICF实验靶图像的代数特征作为RVM的输入样本特征,解决了镜头景深小引起的图像模糊问题。与常用的几种回归算法如支持向量机回归(SVR),K-最近邻法(KNN)及最小均方算法(LMS)进行了实验对比,结果表明,RVM与SVR算法测试误差方差最小,准确率最高,并且几种算法中RVM所用检测时间最短,更适合在线检测。

In order to deal with the problem of the slow on-line testing speed which is caused by the traditional regression model that is not sparse enough,a method based on the relevance vector machine (RVM) regression is proposed in the inertial confinement fusion (ICF) experiment target gesture estimation. In the experiment,the algebra features extracted by the principal component analysis (PCA) is used as the sample features of the RVM,which can deal with the bluer problem caused by the small depth of field. A comparison between the commonly used regression algorithms such as support vector regression (SVR),K-nearest neighbor (KNN) and least mean square (LMS) has been done in this paper,the result shows that the testing error square of the RVM and SVM are the smallest,and the testing accuracy of them are the highest also. The testing time of the RVM is the lowest in the several algorithms,which shows that the RVM is more suitable for on-line testing.