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.
Authors
Additional information
- DOI
- Digital Object Identifier link open in new tab 10.1007/978-3-319-23180-8_8
- Category
- Aktywność konferencyjna
- Type
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Language
- angielski
- Publication year
- 2015
