Multiple Optima in Identification of ARX Models Subject to Missing Data
Abstract Special system identification algorithms are required if there are significant amounts of data missing. Some such algorithms have been developed previously and typically result in iterative procedures for the parameter estimation. Since missing data can be viewed as irregular sampling (deci...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wallin, Ragnar [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2002 |
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| Schlagwörter: |
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| Anmerkung: |
© Wallin and Isaksson 2002 |
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| Übergeordnetes Werk: |
Enthalten in: EURASIP journal on advances in signal processing - Heidelberg : Springer, 2007, 2002(2002), 1 vom: 14. Jan. |
|---|---|
| Übergeordnetes Werk: |
volume:2002 ; year:2002 ; number:1 ; day:14 ; month:01 |
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10.1155/S1110865702000379 |
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| 520 | |a Abstract Special system identification algorithms are required if there are significant amounts of data missing. Some such algorithms have been developed previously and typically result in iterative procedures for the parameter estimation. Since missing data can be viewed as irregular sampling (decimation) of the signals, it is obvious that there is a risk for aliasing. In system identification aliasing manifests itself as potential multiple global optima of the identification loss function. The aim of this paper is to investigate under what circumstances this may in fact occur. The focus of the paper is on periodic missing data patterns. It is shown that it is, in fact, not the fraction of missing data that is important, but rather what time lags of the input and output correlation and cross-correlation functions that can be estimated. For ARX models with all input data observed we verify that there is indeed only one global optimum. | ||
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10.1155/S1110865702000379 doi (DE-627)SPR031971679 (SPR)S1110865702000379-e DE-627 ger DE-627 rakwb eng Wallin, Ragnar verfasserin aut Multiple Optima in Identification of ARX Models Subject to Missing Data 2002 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Wallin and Isaksson 2002 Abstract Special system identification algorithms are required if there are significant amounts of data missing. Some such algorithms have been developed previously and typically result in iterative procedures for the parameter estimation. Since missing data can be viewed as irregular sampling (decimation) of the signals, it is obvious that there is a risk for aliasing. In system identification aliasing manifests itself as potential multiple global optima of the identification loss function. The aim of this paper is to investigate under what circumstances this may in fact occur. The focus of the paper is on periodic missing data patterns. It is shown that it is, in fact, not the fraction of missing data that is important, but rather what time lags of the input and output correlation and cross-correlation functions that can be estimated. For ARX models with all input data observed we verify that there is indeed only one global optimum. parameter estimation (dpeaa)DE-He213 irregular sampling (dpeaa)DE-He213 linear systems (dpeaa)DE-He213 Isaksson, Alf J. aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2002(2002), 1 vom: 14. Jan. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2002 year:2002 number:1 day:14 month:01 https://dx.doi.org/10.1155/S1110865702000379 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_161 GBV_ILN_170 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2522 AR 2002 2002 1 14 01 |
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10.1155/S1110865702000379 doi (DE-627)SPR031971679 (SPR)S1110865702000379-e DE-627 ger DE-627 rakwb eng Wallin, Ragnar verfasserin aut Multiple Optima in Identification of ARX Models Subject to Missing Data 2002 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Wallin and Isaksson 2002 Abstract Special system identification algorithms are required if there are significant amounts of data missing. Some such algorithms have been developed previously and typically result in iterative procedures for the parameter estimation. Since missing data can be viewed as irregular sampling (decimation) of the signals, it is obvious that there is a risk for aliasing. In system identification aliasing manifests itself as potential multiple global optima of the identification loss function. The aim of this paper is to investigate under what circumstances this may in fact occur. The focus of the paper is on periodic missing data patterns. It is shown that it is, in fact, not the fraction of missing data that is important, but rather what time lags of the input and output correlation and cross-correlation functions that can be estimated. For ARX models with all input data observed we verify that there is indeed only one global optimum. parameter estimation (dpeaa)DE-He213 irregular sampling (dpeaa)DE-He213 linear systems (dpeaa)DE-He213 Isaksson, Alf J. aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2002(2002), 1 vom: 14. Jan. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2002 year:2002 number:1 day:14 month:01 https://dx.doi.org/10.1155/S1110865702000379 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_161 GBV_ILN_170 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2522 AR 2002 2002 1 14 01 |
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10.1155/S1110865702000379 doi (DE-627)SPR031971679 (SPR)S1110865702000379-e DE-627 ger DE-627 rakwb eng Wallin, Ragnar verfasserin aut Multiple Optima in Identification of ARX Models Subject to Missing Data 2002 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Wallin and Isaksson 2002 Abstract Special system identification algorithms are required if there are significant amounts of data missing. Some such algorithms have been developed previously and typically result in iterative procedures for the parameter estimation. Since missing data can be viewed as irregular sampling (decimation) of the signals, it is obvious that there is a risk for aliasing. In system identification aliasing manifests itself as potential multiple global optima of the identification loss function. The aim of this paper is to investigate under what circumstances this may in fact occur. The focus of the paper is on periodic missing data patterns. It is shown that it is, in fact, not the fraction of missing data that is important, but rather what time lags of the input and output correlation and cross-correlation functions that can be estimated. For ARX models with all input data observed we verify that there is indeed only one global optimum. parameter estimation (dpeaa)DE-He213 irregular sampling (dpeaa)DE-He213 linear systems (dpeaa)DE-He213 Isaksson, Alf J. aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2002(2002), 1 vom: 14. Jan. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2002 year:2002 number:1 day:14 month:01 https://dx.doi.org/10.1155/S1110865702000379 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_161 GBV_ILN_170 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2522 AR 2002 2002 1 14 01 |
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Multiple Optima in Identification of ARX Models Subject to Missing Data |
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Abstract Special system identification algorithms are required if there are significant amounts of data missing. Some such algorithms have been developed previously and typically result in iterative procedures for the parameter estimation. Since missing data can be viewed as irregular sampling (decimation) of the signals, it is obvious that there is a risk for aliasing. In system identification aliasing manifests itself as potential multiple global optima of the identification loss function. The aim of this paper is to investigate under what circumstances this may in fact occur. The focus of the paper is on periodic missing data patterns. It is shown that it is, in fact, not the fraction of missing data that is important, but rather what time lags of the input and output correlation and cross-correlation functions that can be estimated. For ARX models with all input data observed we verify that there is indeed only one global optimum. © Wallin and Isaksson 2002 |
| abstractGer |
Abstract Special system identification algorithms are required if there are significant amounts of data missing. Some such algorithms have been developed previously and typically result in iterative procedures for the parameter estimation. Since missing data can be viewed as irregular sampling (decimation) of the signals, it is obvious that there is a risk for aliasing. In system identification aliasing manifests itself as potential multiple global optima of the identification loss function. The aim of this paper is to investigate under what circumstances this may in fact occur. The focus of the paper is on periodic missing data patterns. It is shown that it is, in fact, not the fraction of missing data that is important, but rather what time lags of the input and output correlation and cross-correlation functions that can be estimated. For ARX models with all input data observed we verify that there is indeed only one global optimum. © Wallin and Isaksson 2002 |
| abstract_unstemmed |
Abstract Special system identification algorithms are required if there are significant amounts of data missing. Some such algorithms have been developed previously and typically result in iterative procedures for the parameter estimation. Since missing data can be viewed as irregular sampling (decimation) of the signals, it is obvious that there is a risk for aliasing. In system identification aliasing manifests itself as potential multiple global optima of the identification loss function. The aim of this paper is to investigate under what circumstances this may in fact occur. The focus of the paper is on periodic missing data patterns. It is shown that it is, in fact, not the fraction of missing data that is important, but rather what time lags of the input and output correlation and cross-correlation functions that can be estimated. For ARX models with all input data observed we verify that there is indeed only one global optimum. © Wallin and Isaksson 2002 |
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| title_short |
Multiple Optima in Identification of ARX Models Subject to Missing Data |
| url |
https://dx.doi.org/10.1155/S1110865702000379 |
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| author2 |
Isaksson, Alf J. |
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| up_date |
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