摘要
针对软测量方法实际应用中查询样本可能出现奇异点这一问题,提出一种带奇异点检测和补偿的高斯过程回归(Gaussian process regression,GPR)在线软测量方法。该方法首先对训练样本利用高斯过程回归方法进行建模,得到软测量模型;然后对新来查询样本采用改进拉依达准则进行奇异点检测,当新来查询样本被确定为奇异点时,利用辅助模型进行修补,然后再利用软测量模型对修补后查询样本点进行预测;否则,直接对新来查询样本点使用软测量模型进行预测,此方法能够有效确保新来查询样本点的有效性。通过对实际硫回收过程的数据进行实验仿真,进一步验证了所提方法的有效性。
b To handle the problem of the singular query sample which is encountered in the application of soft sensor for the practical industrial processes, an online soft sensor method considering the test and compensation for the singular point is proposed in this paper. A soft sensor model can be built based on the Gaussian process regression （ GPR） approach using the trainingdataset. The pauta criterion is improved to test the new query samples with higher degree of accuracy. If the new query sample is determined as a singular point,an auxiliary model based method is provided to repair the singular point. The renewed query sample is predicted. Otherwise, the GPR soft sensor model can be used to estimate the new query sample directly. I t can ensure the validity of the new query sample point. The effectiveness of the proposed method is verified through the simulation experiment on a real sulfur recovery unit treatment process.
出处
《南京理工大学学报》
EI
CAS
CSCD
北大核心
2017年第4期503-510,共8页
Journal of Nanjing University of Science and Technology
基金
国家自然科学基金(21206053
21276111)
江苏省"六大人才高峰"计划资助项目(2013-DZXX-043)
江苏高校优势学科建设工程资助项目(PAPD)
关键词
高斯过程回归
辅助模型
奇异点
拉依达准则
软测量
Gaussian process regression
auxiliary model
singular point
pauta crite rion
soft sensor
