Iterative-recursive estimation of parameters of regression models with resistance to outliers on practical examples

Here, identification of processes and systems in the sense of the least sum of absolute values is taken into consideration. The respective absolute value estimators are recognised as exceptionally insensitive to large measurement faults or other defects in the processed data, whereas the classical least squares procedure appears to be completely impractical for processing the data contaminated with such parasitic distortions. Since the absolute value quality index cannot be minimised analytically, an iterative solution is used to find optimal estimates of the parameters of the underlying regression model. In addition, an approximate recursive estimator is proposed and implemented for on-line evaluation of system parameters. The convergence (basic property) of the iterative estimator is show to be proven and some aspects related to the absolute value criterion are explained. This allows for the formulation of practical conclusions and indication of directions for further research. In addition, the effectiveness of the described iterative-recursive estimation procedures is practically verified by appropriate numerical experiments. The article considers the identification of processes and systems in the sense of the smallest sum of absolute values, which is extremely insensitive to large measurement errors or other defects of the processed data, while the classical method of least squares seems to be completely impractical when processing data contaminated with such parasitic distortions. image