SCI和EI收录∣中国化工学会会刊

Chinese Journal of Chemical Engineering ›› 2013, Vol. 21 ›› Issue (10): 1144-1154.DOI: 10.1016/S1004-9541(13)60614-X

• 过程系统工程与过程安全 • 上一篇    下一篇

Confidence Level Based on Ridge Estimator in Process Measurement and Its Application

岳元龙, 左信, 罗雄麟   

  1. Department of Automation, China University of Petroleum, Beijing 102249, China
  • 收稿日期:2012-11-23 修回日期:2013-06-07 出版日期:2013-10-28 发布日期:2013-10-29
  • 通讯作者: LUO Xionglin
  • 基金资助:

    Supported by the National Natural Science Foundation of China (21006127) and the National Basic Research Program of China (2012CB720500).

Confidence Level Based on Ridge Estimator in Process Measurement and Its Application

YUE Yuanlong, ZUO Xin, LUO Xionglin   

  1. Department of Automation, China University of Petroleum, Beijing 102249, China
  • Received:2012-11-23 Revised:2013-06-07 Online:2013-10-28 Published:2013-10-29
  • Contact: LUO Xionglin
  • Supported by:

    Supported by the National Natural Science Foundation of China (21006127) and the National Basic Research Program of China (2012CB720500).

摘要: Ordinary least squares (OLS) algorithm is widely applied in process measurement, because the sensor model used to estimate unknown parameters can be approximated through multivariate linear model. However, with few or noisy data or multi-collinearity, unbiased OLS leads to large variance. Biased estimators, especially ridge estimator, have been introduced to improve OLS by trading bias for variance. Ridge estimator is feasible as an estimator with smaller variance. At the same confidence level, with additive noise as the normal random variable, the less variance one estimator has, the shorter the two-sided symmetric confidence interval is. However, this finding is limited to the unbiased estimator and few studies analyze and compare the confidence levels between ridge estimator and OLS. This paper derives the matrix of ridge parameters under necessary and sufficient conditions based on which ridge estimator is superior to OLS in terms of mean squares error matrix, rather than mean squares error. Then the confidence levels between ridge estimator and OLS are compared under the condition of OLS fixed symmetric confidence interval, rather than the criteria for evaluating the validity of different unbiased estimators. We conclude that the confidence level of ridge estimator can not be directly compared with that of OLS based on the criteria available for unbiased estimators, which is verified by a simulation and a laboratory scale experiment on a single parameter measurement.

关键词: process measurement, confidence level, ridge estimator, variance, bias

Abstract: Ordinary least squares (OLS) algorithm is widely applied in process measurement, because the sensor model used to estimate unknown parameters can be approximated through multivariate linear model. However, with few or noisy data or multi-collinearity, unbiased OLS leads to large variance. Biased estimators, especially ridge estimator, have been introduced to improve OLS by trading bias for variance. Ridge estimator is feasible as an estimator with smaller variance. At the same confidence level, with additive noise as the normal random variable, the less variance one estimator has, the shorter the two-sided symmetric confidence interval is. However, this finding is limited to the unbiased estimator and few studies analyze and compare the confidence levels between ridge estimator and OLS. This paper derives the matrix of ridge parameters under necessary and sufficient conditions based on which ridge estimator is superior to OLS in terms of mean squares error matrix, rather than mean squares error. Then the confidence levels between ridge estimator and OLS are compared under the condition of OLS fixed symmetric confidence interval, rather than the criteria for evaluating the validity of different unbiased estimators. We conclude that the confidence level of ridge estimator can not be directly compared with that of OLS based on the criteria available for unbiased estimators, which is verified by a simulation and a laboratory scale experiment on a single parameter measurement.

Key words: process measurement, confidence level, ridge estimator, variance, bias