Research on Non-linear Rectification of Sensor Based on Improved LS-SVR

摘要：

An improved least squares support vector regression machine (LS-SVR) is put forward and applied to non-linearity calibration of a thermocouple sensor. Solving a standard LS-SVR involves inverting a N dimensional matrix square matrix where N is the number of training samples, and it can considerably be a formidable problem when N is augmented. Sherman-Morrison-Woodbury (SMW) transformation is introduced into the solution of LS-SVR, by which the N dimensional matrix can be inverted by inverting instead another M dimensional matrix where M is the dimension of training samples. So, the large sample regression problem based on LSSVR is solved. Finally, the non-linearity calibration of a platinum-rhodium 30-platinum-rhodium 6 thermocouple (B) sensor is taken as an example, and standard LS-SVR and improved one are used to identify the coefficients of power series-based calibration model respectively. Experimental results show that the time complexity of the improved LS-SVR-based calibration method is hardly influenced by the number of calibration data points. The method suggested may be also used for other similar applications.

关键词： Least squares support vector regression (LS-SVR) Sherman-Morrison-Woodbury (SMW) Sensor Non-linearity calibration

作者: Tong Shu

作者单位: Key Laboratory of Numerical Control of Jiangxi Province, Jiujiang University, Jiujiang 332005, China School of Electronic and Information Engineering; Dalian University of Technology, Dalian, 116024, China

会议类型: 国际会议

会议名称: 2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

会议地点: 广西桂林

会议语种:英文

页码: 4475-4480

在线出版日期: 2009-06-17（万方平台首次上网日期，不代表论文的发表时间）