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
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.
