Because of the presence of sporadic high-intensity measurement noise （outliers）, an adaptive algorithm for the robust estimation of parameters of linear dynamic discrete-time systems is proposed in this paper. First...Because of the presence of sporadic high-intensity measurement noise （outliers）, an adaptive algorithm for the robust estimation of parameters of linear dynamic discrete-time systems is proposed in this paper. First, the sorted data versus the normal quantiles is plotted, called QQ-plot. Next, the e-contaminated normal distribution of noise is adopted. Then, a data classification procedure based on the QQ-plot approach, combined with the robustified data winsorization technique, is developed; the estimation of the unknown noise statistical parameters is solved. Moreover, an iterative procedure for estimating the contamination degree ~＇, which originated from an ML classification, is also proposed. Thus, an ^-contaminated noise distribution is estimated and, the suboptimal maximum likelihood criterion is defined, and the system-parameter estimation problem is solved robustly, using the proposed recursive robust parameter estimation scheme. Finally, these parameters are used to estimate water level in the steam drum and residual of the steam-drum water level sensor.展开更多
桥梁健康监测系统以传感器测得的结构响应识别桥梁结构的模态参数,从而对损伤进行预警。然而,由于测试环境的限制,传感器老旧、损坏等情况时常发生,使得测试结果受到影响。结合探索性数据分析(Exploratory Data Analysis,EDA)方法,采用...桥梁健康监测系统以传感器测得的结构响应识别桥梁结构的模态参数,从而对损伤进行预警。然而,由于测试环境的限制,传感器老旧、损坏等情况时常发生,使得测试结果受到影响。结合探索性数据分析(Exploratory Data Analysis,EDA)方法,采用箱形图和QQ图对测试结果进行了跟踪,对分析结果异常的数据对应的传感器进行了修复,并对比了修复前后的模态参数识别结果。结果表明,经过本文所提方法处理后的测试结果能提供更为准确的模态参数识别结果,提高桥梁健康监测的准确度和效率。展开更多
The structural equation model (SEM) concept is generally influenced by the presence of outliers and controlling variables. To a very large extent, this could have consequential effects on the parameters and the model ...The structural equation model (SEM) concept is generally influenced by the presence of outliers and controlling variables. To a very large extent, this could have consequential effects on the parameters and the model fitness. Though previous researches have studied outliers and controlling observations from various perspectives including the use of box plots, normal probability plots, among others, the use of uniform horizontal QQ plot is yet to be explored. This study is, therefore, aimed at applying uniform QQ plots to identifying outliers and possible controlling observations in SEM. The results showed that all the three methods of estimators manifest the ability to identify outliers and possible controlling observations in SEM. It was noted that the Anderson-Rubin estimator of QQ plot showed a more efficient or visual display of spotting outliers and possible controlling observations as compared to the other methods of estimators. Therefore, this paper provides an efficient way identifying outliers as it fragments the data set.展开更多
文摘Because of the presence of sporadic high-intensity measurement noise （outliers）, an adaptive algorithm for the robust estimation of parameters of linear dynamic discrete-time systems is proposed in this paper. First, the sorted data versus the normal quantiles is plotted, called QQ-plot. Next, the e-contaminated normal distribution of noise is adopted. Then, a data classification procedure based on the QQ-plot approach, combined with the robustified data winsorization technique, is developed; the estimation of the unknown noise statistical parameters is solved. Moreover, an iterative procedure for estimating the contamination degree ~＇, which originated from an ML classification, is also proposed. Thus, an ^-contaminated noise distribution is estimated and, the suboptimal maximum likelihood criterion is defined, and the system-parameter estimation problem is solved robustly, using the proposed recursive robust parameter estimation scheme. Finally, these parameters are used to estimate water level in the steam drum and residual of the steam-drum water level sensor.
文摘桥梁健康监测系统以传感器测得的结构响应识别桥梁结构的模态参数,从而对损伤进行预警。然而,由于测试环境的限制,传感器老旧、损坏等情况时常发生,使得测试结果受到影响。结合探索性数据分析(Exploratory Data Analysis,EDA)方法,采用箱形图和QQ图对测试结果进行了跟踪,对分析结果异常的数据对应的传感器进行了修复,并对比了修复前后的模态参数识别结果。结果表明,经过本文所提方法处理后的测试结果能提供更为准确的模态参数识别结果,提高桥梁健康监测的准确度和效率。
文摘The structural equation model (SEM) concept is generally influenced by the presence of outliers and controlling variables. To a very large extent, this could have consequential effects on the parameters and the model fitness. Though previous researches have studied outliers and controlling observations from various perspectives including the use of box plots, normal probability plots, among others, the use of uniform horizontal QQ plot is yet to be explored. This study is, therefore, aimed at applying uniform QQ plots to identifying outliers and possible controlling observations in SEM. The results showed that all the three methods of estimators manifest the ability to identify outliers and possible controlling observations in SEM. It was noted that the Anderson-Rubin estimator of QQ plot showed a more efficient or visual display of spotting outliers and possible controlling observations as compared to the other methods of estimators. Therefore, this paper provides an efficient way identifying outliers as it fragments the data set.
