Nonlinear Continuous System Identification by Means of Multiple Integration II
This paper presents a new modification of the multiple integration method [1, 2, 3] for continuous nonlinear SISO system identification from measured input - output data. The model structure is changed compared with [1]. This change enables more sophisticated systems to be identified. The resulting...
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| Format: | Article |
| Language: | English |
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CTU Central Library
2001-01-01
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| Series: | Acta Polytechnica |
| Subjects: | |
| Online Access: | https://ojs.cvut.cz/ojs/index.php/ap/article/view/200 |
| _version_ | 1818973765823889408 |
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| author | J. John |
| author_facet | J. John |
| author_sort | J. John |
| collection | DOAJ |
| description | This paper presents a new modification of the multiple integration method [1, 2, 3] for continuous nonlinear SISO system identification from measured input - output data. The model structure is changed compared with [1]. This change enables more sophisticated systems to be identified. The resulting MATLAB program is available in [4]. As was stated in [1], there is no need to reach a steady state of the identified system. The algorithm also automatically filters the measured data with respect to low frequency drifts and offsets, and offers the user a potent tool for selecting the frequency range of validity of the obtained model. |
| first_indexed | 2024-12-20T15:29:22Z |
| format | Article |
| id | doaj.art-f7489b557ec84fb68f42a5d3fb7d4fa1 |
| institution | Directory Open Access Journal |
| issn | 1210-2709 1805-2363 |
| language | English |
| last_indexed | 2024-12-20T15:29:22Z |
| publishDate | 2001-01-01 |
| publisher | CTU Central Library |
| record_format | Article |
| series | Acta Polytechnica |
| spelling | doaj.art-f7489b557ec84fb68f42a5d3fb7d4fa12022-12-21T19:35:40ZengCTU Central LibraryActa Polytechnica1210-27091805-23632001-01-01411200Nonlinear Continuous System Identification by Means of Multiple Integration IIJ. JohnThis paper presents a new modification of the multiple integration method [1, 2, 3] for continuous nonlinear SISO system identification from measured input - output data. The model structure is changed compared with [1]. This change enables more sophisticated systems to be identified. The resulting MATLAB program is available in [4]. As was stated in [1], there is no need to reach a steady state of the identified system. The algorithm also automatically filters the measured data with respect to low frequency drifts and offsets, and offers the user a potent tool for selecting the frequency range of validity of the obtained model.https://ojs.cvut.cz/ojs/index.php/ap/article/view/200continuous system identificationmultiple integration |
| spellingShingle | J. John Nonlinear Continuous System Identification by Means of Multiple Integration II Acta Polytechnica continuous system identification multiple integration |
| title | Nonlinear Continuous System Identification by Means of Multiple Integration II |
| title_full | Nonlinear Continuous System Identification by Means of Multiple Integration II |
| title_fullStr | Nonlinear Continuous System Identification by Means of Multiple Integration II |
| title_full_unstemmed | Nonlinear Continuous System Identification by Means of Multiple Integration II |
| title_short | Nonlinear Continuous System Identification by Means of Multiple Integration II |
| title_sort | nonlinear continuous system identification by means of multiple integration ii |
| topic | continuous system identification multiple integration |
| url | https://ojs.cvut.cz/ojs/index.php/ap/article/view/200 |
| work_keys_str_mv | AT jjohn nonlinearcontinuoussystemidentificationbymeansofmultipleintegrationii |