Dynamic Error Correction of Measuring System Using Support Vector Machine
The support vector machine (SVM) is proposed for dynamic error correction of measuring systems. The SVM is established based on the structural risk minimization principle rather than minimize the empirical error commonly implemented in the neural networks. Hence, the SVM can overcome the shortcoming of neural networks in dynamic error correction of measuring systems. The feasibility and efficacy of the method are demonstrated by applying it to an example. The results show that the proposed method is still effective even if there is additive measuring noise.
error correction measuring system support vector machine
WANG Xiaodong ZHANG Haoran ZHANG Changjiang WANG Jinshan YE Meiying
Department of Electronic Engineering, Zhejiang Normal University, Jinhua 321004, China
国际会议
北京
英文
2007-08-05(万方平台首次上网日期,不代表论文的发表时间)