Nonlinear Correction for Thermocouple Vacuum Sensor Based on Multiple Support Vector Regressions
It is an efficient approach of nonlinear correction for sensors by fitting curves to data, but it is difficult to fit curve with a single model for the thermocouple vacuum sensor,since its characteristic is very complex. A novel method of curve fitting based on multiple support vector regression (MSVR) is proposed. The sample space are divided into several sub-spaces, nonlinear mapping is established in each sub-space by using support vector regressions (SVR), each SVR may has parameters different from the others to approximate the sensor characteristic in corresponding partial space, combining the outputs of all the SVRs the complete characteristic of the sensor is achieved. The proposed approach was applied to fit the characteristic curve for the thermocouple vacuum sensor, the experiment results demonstrate the effectiveness and practicability of the proposed method.
Multiple support vector regression (MSVR) Samples division Fitting Nonlinear correction
Gao Feiyan Tang Yaogeng Luo Liang
University of South China, Hengyang 421001, China Southwest University of Science and Technology, Mianyang 621010, China
国际会议
长沙
英文
1906-1909
2010-03-13（万方平台首次上网日期，不代表论文的发表时间）