Discrete-time neural observers: analysis and applications
Discrete-Time Neural Observers: Analysis and Applications presents recent advances in the theory of neural state estimation for discrete-time unknown nonlinear systems with multiple inputs and outputs. The book includes rigorous mathematical analyses, based on the Lyapunov approach, that guarantee t...
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| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
London
Academic Press, an imprint of Elsevier
[2017]
|
| Schlagworte: | |
| Online-Zugang: | FLA01 URL des Erstveröffentlichers |
| Zusammenfassung: | Discrete-Time Neural Observers: Analysis and Applications presents recent advances in the theory of neural state estimation for discrete-time unknown nonlinear systems with multiple inputs and outputs. The book includes rigorous mathematical analyses, based on the Lyapunov approach, that guarantee their properties. In addition, for each chapter, simulation results are included to verify the successful performance of the corresponding proposed schemes. In order to complete the treatment of these schemes, the authors also present simulation and experimental results related to their application in meaningful areas, such as electric three phase induction motors and anaerobic process, which show the applicability of such designs. The proposed schemes can be employed for different applications beyond those presented. The book presents solutions for the state estimation problem of unknown nonlinear systems based on two schemes. For the first one, a full state estimation problem is considered; the second one considers the reduced order case with, and without, the presence of unknown delays. Both schemes are developed in discrete-time using recurrent high order neural networks in order to design the neural observers, and the online training of the respective neural networks is performed by Kalman Filtering |
| Beschreibung: | Includes bibliographical references adn index |
| Beschreibung: | 1 online resource illustrations |
| ISBN: | 9780128105443 0128105445 |
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| 245 | 1 | 0 | |a Discrete-time neural observers |b analysis and applications |c Alma Y. Alanis, Edgar N. Sanchez |
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| 500 | |a Includes bibliographical references adn index | ||
| 520 | |a Discrete-Time Neural Observers: Analysis and Applications presents recent advances in the theory of neural state estimation for discrete-time unknown nonlinear systems with multiple inputs and outputs. The book includes rigorous mathematical analyses, based on the Lyapunov approach, that guarantee their properties. In addition, for each chapter, simulation results are included to verify the successful performance of the corresponding proposed schemes. In order to complete the treatment of these schemes, the authors also present simulation and experimental results related to their application in meaningful areas, such as electric three phase induction motors and anaerobic process, which show the applicability of such designs. The proposed schemes can be employed for different applications beyond those presented. The book presents solutions for the state estimation problem of unknown nonlinear systems based on two schemes. For the first one, a full state estimation problem is considered; the second one considers the reduced order case with, and without, the presence of unknown delays. Both schemes are developed in discrete-time using recurrent high order neural networks in order to design the neural observers, and the online training of the respective neural networks is performed by Kalman Filtering | ||
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Datensatz im Suchindex
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| author | Alanis, Alma Y. Sanchez, Edgar N. |
| author_facet | Alanis, Alma Y. Sanchez, Edgar N. |
| author_role | aut aut |
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| dewey-full | 003.75 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 003 - Systems |
| dewey-raw | 003.75 |
| dewey-search | 003.75 |
| dewey-sort | 13.75 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
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| id | DE-604.BV045131690 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T08:09:35Z |
| institution | BVB |
| isbn | 9780128105443 0128105445 |
| language | English |
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| publishDate | 2017 |
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| publisher | Academic Press, an imprint of Elsevier |
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| spelling | Alanis, Alma Y. Verfasser aut Discrete-time neural observers analysis and applications Alma Y. Alanis, Edgar N. Sanchez London Academic Press, an imprint of Elsevier [2017] © 2017 1 online resource illustrations txt rdacontent c rdamedia cr rdacarrier Includes bibliographical references adn index Discrete-Time Neural Observers: Analysis and Applications presents recent advances in the theory of neural state estimation for discrete-time unknown nonlinear systems with multiple inputs and outputs. The book includes rigorous mathematical analyses, based on the Lyapunov approach, that guarantee their properties. In addition, for each chapter, simulation results are included to verify the successful performance of the corresponding proposed schemes. In order to complete the treatment of these schemes, the authors also present simulation and experimental results related to their application in meaningful areas, such as electric three phase induction motors and anaerobic process, which show the applicability of such designs. The proposed schemes can be employed for different applications beyond those presented. The book presents solutions for the state estimation problem of unknown nonlinear systems based on two schemes. For the first one, a full state estimation problem is considered; the second one considers the reduced order case with, and without, the presence of unknown delays. Both schemes are developed in discrete-time using recurrent high order neural networks in order to design the neural observers, and the online training of the respective neural networks is performed by Kalman Filtering SCIENCE / System Theory bisacsh TECHNOLOGY & ENGINEERING / Operations Research bisacsh Discrete-time systems fast Neural networks (Computer science) fast Nonlinear control theory fast Discrete-time systems Nonlinear control theory Neural networks (Computer science) Sanchez, Edgar N. aut Erscheint auch als Druck-Ausgabe 9780128105436 Erscheint auch als Druck-Ausgabe 0128105437 http://www.sciencedirect.com/science/book/9780128105436 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Alanis, Alma Y. Sanchez, Edgar N. Discrete-time neural observers analysis and applications SCIENCE / System Theory bisacsh TECHNOLOGY & ENGINEERING / Operations Research bisacsh Discrete-time systems fast Neural networks (Computer science) fast Nonlinear control theory fast Discrete-time systems Nonlinear control theory Neural networks (Computer science) |
| title | Discrete-time neural observers analysis and applications |
| title_auth | Discrete-time neural observers analysis and applications |
| title_exact_search | Discrete-time neural observers analysis and applications |
| title_full | Discrete-time neural observers analysis and applications Alma Y. Alanis, Edgar N. Sanchez |
| title_fullStr | Discrete-time neural observers analysis and applications Alma Y. Alanis, Edgar N. Sanchez |
| title_full_unstemmed | Discrete-time neural observers analysis and applications Alma Y. Alanis, Edgar N. Sanchez |
| title_short | Discrete-time neural observers |
| title_sort | discrete time neural observers analysis and applications |
| title_sub | analysis and applications |
| topic | SCIENCE / System Theory bisacsh TECHNOLOGY & ENGINEERING / Operations Research bisacsh Discrete-time systems fast Neural networks (Computer science) fast Nonlinear control theory fast Discrete-time systems Nonlinear control theory Neural networks (Computer science) |
| topic_facet | SCIENCE / System Theory TECHNOLOGY & ENGINEERING / Operations Research Discrete-time systems Neural networks (Computer science) Nonlinear control theory |
| url | http://www.sciencedirect.com/science/book/9780128105436 |
| work_keys_str_mv | AT alanisalmay discretetimeneuralobserversanalysisandapplications AT sanchezedgarn discretetimeneuralobserversanalysisandapplications |