Subspace Identification for Linear Systems: Theory - Implementation - Applications
Subspace Identification for Linear Systems focuses on the theory, implementation and applications of subspace identification algorithms for linear time-invariant finite- dimensional dynamical systems. These algorithms allow for a fast, straightforward and accurate determination of linear multivariab...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
1996
|
| Schlagworte: | |
| Online-Zugang: | BTU01 FHI01 Volltext |
| Zusammenfassung: | Subspace Identification for Linear Systems focuses on the theory, implementation and applications of subspace identification algorithms for linear time-invariant finite- dimensional dynamical systems. These algorithms allow for a fast, straightforward and accurate determination of linear multivariable models from measured input-output data. The theory of subspace identification algorithms is presented in detail. Several chapters are devoted to deterministic, stochastic and combined deterministic-stochastic subspace identification algorithms. For each case, the geometric properties are stated in a main 'subspace' Theorem. Relations to existing algorithms and literature are explored, as are the interconnections between different subspace algorithms. The subspace identification theory is linked to the theory of frequency weighted model reduction, which leads to new interpretations and insights. The implementation of subspace identification algorithms is discussed in terms of the robust and computationally efficient RQ and singular value decompositions, which are well-established algorithms from numerical linear algebra. The algorithms are implemented in combination with a whole set of classical identification algorithms, processing and validation tools in Xmath's ISID, a commercially available graphical user interface toolbox. The basic subspace algorithms in the book are also implemented in a set of Matlab files accompanying the book. An application of ISID to an industrial glass tube manufacturing process is presented in detail, illustrating the power and user-friendliness of the subspace identification algorithms and of their implementation in ISID. The identified model allows for an optimal control of the process, leading to a significant enhancement of the production quality. The applicability of subspace identification algorithms in industry is further illustrated with the application of the Matlab files to ten practical problems. Since all necessary data and Matlab files are included, the reader can easily step through these applications, and thus get more insight in the algorithms. Subspace Identification for Linear Systems is an important reference for all researchers in system theory, control theory, signal processing, automization, mechatronics, chemical, electrical, mechanical and aeronautical engineering |
| Beschreibung: | 1 Online-Ressource (272 p) |
| ISBN: | 9781461304654 |
| DOI: | 10.1007/978-1-4613-0465-4 |
Internformat
MARC
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| 520 | |a Subspace Identification for Linear Systems focuses on the theory, implementation and applications of subspace identification algorithms for linear time-invariant finite- dimensional dynamical systems. These algorithms allow for a fast, straightforward and accurate determination of linear multivariable models from measured input-output data. The theory of subspace identification algorithms is presented in detail. Several chapters are devoted to deterministic, stochastic and combined deterministic-stochastic subspace identification algorithms. For each case, the geometric properties are stated in a main 'subspace' Theorem. Relations to existing algorithms and literature are explored, as are the interconnections between different subspace algorithms. The subspace identification theory is linked to the theory of frequency weighted model reduction, which leads to new interpretations and insights. | ||
| 520 | |a The implementation of subspace identification algorithms is discussed in terms of the robust and computationally efficient RQ and singular value decompositions, which are well-established algorithms from numerical linear algebra. The algorithms are implemented in combination with a whole set of classical identification algorithms, processing and validation tools in Xmath's ISID, a commercially available graphical user interface toolbox. The basic subspace algorithms in the book are also implemented in a set of Matlab files accompanying the book. An application of ISID to an industrial glass tube manufacturing process is presented in detail, illustrating the power and user-friendliness of the subspace identification algorithms and of their implementation in ISID. The identified model allows for an optimal control of the process, leading to a significant enhancement of the production quality. | ||
| 520 | |a The applicability of subspace identification algorithms in industry is further illustrated with the application of the Matlab files to ten practical problems. Since all necessary data and Matlab files are included, the reader can easily step through these applications, and thus get more insight in the algorithms. Subspace Identification for Linear Systems is an important reference for all researchers in system theory, control theory, signal processing, automization, mechatronics, chemical, electrical, mechanical and aeronautical engineering | ||
| 650 | 4 | |a Engineering | |
| 650 | 4 | |a Electrical Engineering | |
| 650 | 4 | |a Systems Theory, Control | |
| 650 | 4 | |a Signal, Image and Speech Processing | |
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Datensatz im Suchindex
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| author | Overschee, Peter Van Moor, Bart De |
| author_facet | Overschee, Peter Van Moor, Bart De |
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| dewey-full | 621.3 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 621 - Applied physics |
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| dewey-search | 621.3 |
| dewey-sort | 3621.3 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Elektrotechnik / Elektronik / Nachrichtentechnik |
| doi_str_mv | 10.1007/978-1-4613-0465-4 |
| format | Electronic eBook |
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| id | DE-604.BV045186941 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:10:58Z |
| institution | BVB |
| isbn | 9781461304654 |
| language | English |
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| physical | 1 Online-Ressource (272 p) |
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| spelling | Overschee, Peter Van Verfasser aut Subspace Identification for Linear Systems Theory - Implementation - Applications by Peter Van Overschee, Bart De Moor Boston, MA Springer US 1996 1 Online-Ressource (272 p) txt rdacontent c rdamedia cr rdacarrier Subspace Identification for Linear Systems focuses on the theory, implementation and applications of subspace identification algorithms for linear time-invariant finite- dimensional dynamical systems. These algorithms allow for a fast, straightforward and accurate determination of linear multivariable models from measured input-output data. The theory of subspace identification algorithms is presented in detail. Several chapters are devoted to deterministic, stochastic and combined deterministic-stochastic subspace identification algorithms. For each case, the geometric properties are stated in a main 'subspace' Theorem. Relations to existing algorithms and literature are explored, as are the interconnections between different subspace algorithms. The subspace identification theory is linked to the theory of frequency weighted model reduction, which leads to new interpretations and insights. The implementation of subspace identification algorithms is discussed in terms of the robust and computationally efficient RQ and singular value decompositions, which are well-established algorithms from numerical linear algebra. The algorithms are implemented in combination with a whole set of classical identification algorithms, processing and validation tools in Xmath's ISID, a commercially available graphical user interface toolbox. The basic subspace algorithms in the book are also implemented in a set of Matlab files accompanying the book. An application of ISID to an industrial glass tube manufacturing process is presented in detail, illustrating the power and user-friendliness of the subspace identification algorithms and of their implementation in ISID. The identified model allows for an optimal control of the process, leading to a significant enhancement of the production quality. The applicability of subspace identification algorithms in industry is further illustrated with the application of the Matlab files to ten practical problems. Since all necessary data and Matlab files are included, the reader can easily step through these applications, and thus get more insight in the algorithms. Subspace Identification for Linear Systems is an important reference for all researchers in system theory, control theory, signal processing, automization, mechatronics, chemical, electrical, mechanical and aeronautical engineering Engineering Electrical Engineering Systems Theory, Control Signal, Image and Speech Processing Mechanical Engineering System theory Mechanical engineering Electrical engineering Moor, Bart De aut Erscheint auch als Druck-Ausgabe 9781461380610 https://doi.org/10.1007/978-1-4613-0465-4 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Overschee, Peter Van Moor, Bart De Subspace Identification for Linear Systems Theory - Implementation - Applications Engineering Electrical Engineering Systems Theory, Control Signal, Image and Speech Processing Mechanical Engineering System theory Mechanical engineering Electrical engineering |
| title | Subspace Identification for Linear Systems Theory - Implementation - Applications |
| title_auth | Subspace Identification for Linear Systems Theory - Implementation - Applications |
| title_exact_search | Subspace Identification for Linear Systems Theory - Implementation - Applications |
| title_full | Subspace Identification for Linear Systems Theory - Implementation - Applications by Peter Van Overschee, Bart De Moor |
| title_fullStr | Subspace Identification for Linear Systems Theory - Implementation - Applications by Peter Van Overschee, Bart De Moor |
| title_full_unstemmed | Subspace Identification for Linear Systems Theory - Implementation - Applications by Peter Van Overschee, Bart De Moor |
| title_short | Subspace Identification for Linear Systems |
| title_sort | subspace identification for linear systems theory implementation applications |
| title_sub | Theory - Implementation - Applications |
| topic | Engineering Electrical Engineering Systems Theory, Control Signal, Image and Speech Processing Mechanical Engineering System theory Mechanical engineering Electrical engineering |
| topic_facet | Engineering Electrical Engineering Systems Theory, Control Signal, Image and Speech Processing Mechanical Engineering System theory Mechanical engineering Electrical engineering |
| url | https://doi.org/10.1007/978-1-4613-0465-4 |
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