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Abstract
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.
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- Accepted or Published Version
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- Copyright (2017 IEEE)
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- Category:
- Conference activity
- Type:
- materiały konferencyjne indeksowane w Web of Science
- Title of issue:
- 40th International Conference on Telecommunications and Signal Processing (TSP) strony 237 - 242
- Language:
- English
- Publication year:
- 2017
- Bibliographic description:
- Niedźwiecki M., Ciołek M..: Akaike's final prediction error criterion revisited, W: 40th International Conference on Telecommunications and Signal Processing (TSP), 2017, ,.
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/tsp.2017.8075977
- Verified by:
- Gdańsk University of Technology
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