When local identification of a nonstationary ARX system is carried out, two important decisions must be taken. First, one should decide upon the number of estimated parameters, i.e., on the model order. Second, one should choose the appropriate estimation bandwidth, related to the (effective) number of input-output data samples that will be used for identification/ tracking purposes. Failure to make the right decisions results in the model deterioration, both in the quantitative and qualitative sense. In this paper, we show that both problems can be solved using the suitably modified Akaike’s final prediction error criterion. The proposed solution is next compared with another one, based on the Rissanen’s predictive least squares principle.
Authors
Additional information
- DOI
- Digital Object Identifier link open in new tab 10.1109/tsp.2017.8075977
- Category
- Aktywność konferencyjna
- Type
- materiały konferencyjne indeksowane w Web of Science
- Language
- angielski
- Publication year
- 2017
