Identification of time-varying Hammerstein systems from ensemble data
In this paper, we describe a new technique to identify rapidly time-varying Hammerstein systems from ensembles of input-output realizations. The technique involves two steps. A correlation approach is first used to obtain initial estimates of the linear subsystem parameters for every sampling time....
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| Published in | Annals of biomedical engineering Vol. 29; no. 7; pp. 619 - 635 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Springer Nature B.V
01.07.2001
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| Online Access | Get full text |
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| Abstract | In this paper, we describe a new technique to identify rapidly time-varying Hammerstein systems from ensembles of input-output realizations. The technique involves two steps. A correlation approach is first used to obtain initial estimates of the linear subsystem parameters for every sampling time. An iterative optimization algorithm is then employed to produce final estimates of the system parameters. The input does not need to be white. The technique was tested on simulated data and was found to produce excellent results under realistic conditions. |
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| AbstractList | In this paper, we describe a new technique to identify rapidly time-varying Hammerstein systems from ensembles of input-output realizations. The technique involves two steps. A correlation approach is first used to obtain initial estimates of the linear subsystem parameters for every sampling time. An iterative optimization algorithm is then employed to produce final estimates of the system parameters. The input does not need to be white. The technique was tested on simulated data and was found to produce excellent results under realistic conditions. In this paper, we describe a new technique to identify rapidly time-varying Hammerstein systems from ensembles of input-output realizations. The technique involves two steps. A correlation approach is first used to obtain initial estimates of the linear subsystem parameters for every sampling time. An iterative optimization algorithm is then employed to produce final estimates of the system parameters. The input does not need to be white. The technique was tested on simulated data and was found to produce excellent results under realistic conditions. [copy 2001 Biomedical Engineering Society. PAC01: 8710+e, 0545Tp In this paper, we describe a new technique to identify rapidly time-varying Hammerstein systems from ensembles of input-output realizations. The technique involves two steps. A correlation approach is first used to obtain initial estimates of the linear subsystem parameters for every sampling time. An iterative optimization algorithm is then employed to produce final estimates of the system parameters. The input does not need to be white. The technique was tested on simulated data and was found to produce excellent results under realistic conditions. © 2001 Biomedical Engineering Society. PAC01: 8710+e, 0545Tp[PUBLICATION ABSTRACT] A new technique to identify rapidly time-varying Hammerstein systems from ensemble data is presented. The technique involves two steps. First, a correlation approach is used to obtain an initial estimate of the linear dynamics for every instant. Second, an iterative optimization algorithm is employed to generate final estimates of the system parameters. |
| Author | Kearney, R E Lortie, M |
| Author_xml | – sequence: 1 givenname: M surname: Lortie fullname: Lortie, M organization: Department of Biomedical Engineering, McGill University, Montréal, Québec, Canada – sequence: 2 givenname: R E surname: Kearney fullname: Kearney, R E |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/11501626$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1109_TBME_2011_2113184 crossref_primary_10_3182_20100901_3_IT_2016_00137 crossref_primary_10_1016_j_ifacol_2015_12_265 crossref_primary_10_1016_j_ifacol_2015_12_217 crossref_primary_10_1016_j_ifacol_2015_12_124 crossref_primary_10_1109_ACCESS_2022_3141704 crossref_primary_10_2478_v10006_010_0038_y crossref_primary_10_3182_20080706_5_KR_1001_01327 |
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| Snippet | In this paper, we describe a new technique to identify rapidly time-varying Hammerstein systems from ensembles of input-output realizations. The technique... A new technique to identify rapidly time-varying Hammerstein systems from ensemble data is presented. The technique involves two steps. First, a correlation... |
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| Title | Identification of time-varying Hammerstein systems from ensemble data |
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