In this paper estimation algorithms derived in the sense of the least sum of absolute errors are considered for the purpose of identification of models and signals. In particular, off-line and approximate on-line estimation schemes discussed in the work are aimed at both assessing the coefficients of discrete-time stationary models and tracking the evolution of time-variant characteristics of monitored signals. What is interesting, the procedures resulting from minimization of absolute-error criteria appear to be insensitive to sporadic outliers in the processed data. With this fundamental property the deliberated absolute-error method provides correct results of identification, while the classical least-squares estimation produces outcomes, which are definitely unreliable in such circumstances. The quality of estimation and the robustness of the discussed identification procedures to occasional measurement faults are demonstrated in a few practical numerical tests.
CITATION STYLE
Kozłowski, J., & Kowalczuk, Z. (2015). Identification of models and signals robust to occasional outliers. In Advanced and Intelligent Computations in Diagnosis and Control (pp. 105–117). Springer International Publishing. https://doi.org/10.1007/978-3-319-23180-8_8
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