A Novel Soft Sensor Modelling Method Based on Kernel PLS

摘要：

A novel soft sensor modeling method based on kernel partial least squares (kernel PLS, KPLS) was proposed. Kernel PLS is a promising regression method for tackling nonlinear problems because it can efficiently compute regression coefficients in highdimensional feature space by means of nonlinear kernel function. Application results to the real data in a fluid catalytic cracking unit (FCCU) process show that the proposed method can effectively capture nonlinear relationship among variables and have better estimation performance than PLS and other linear approaches.

关键词： Nonlinear Soft sensor Kernel partial least squares (KPLS) Quality estimation

作者: Xi Zhang Weijian Huang Yaqing Zhu Shihe Chen

作者单位: Guangdong Electric Power Research Institute Guangzhou 510080, China Guangdong Electric Power Research Institute Guangzhou 510080,China

会议类型: 国际会议

会议名称: 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems(2010 IEEE 智能计算与智能系统国际会议 ICIS 2010)

会议地点: 厦门

会议语种:英文

页码: 295-299

在线出版日期: 2010-10-29（万方平台首次上网日期，不代表论文的发表时间）