Adaptive Control with Recurrent High-order Neural Networks: Theory and Industrial Applications
The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer met...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
London
Springer London
2000
|
| Ausgabe: | 1st ed. 2000 |
| Schriftenreihe: | Advances in Industrial Control
|
| Schlagworte: | |
| Online-Zugang: | UBY01 Volltext |
| Zusammenfassung: | The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies ... , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. Neural networks is one of those areas where an initial burst of enthusiasm and optimism leads to an explosion of papers in the journals and many presentations at conferences but it is only in the last decade that significant theoretical work on stability, convergence and robustness for the use of neural networks in control systems has been tackled. George Rovithakis and Manolis Christodoulou have been interested in these theoretical problems and in the practical aspects of neural network applications to industrial problems. This very welcome addition to the Advances in Industrial Control series provides a succinct report of their research. The neural network model at the core of their work is the Recurrent High Order Neural Network (RHONN) and a complete theoretical and simulation development is presented. Different readers will find different aspects of the development of interest. The last chapter of the monograph discusses the problem of manufacturing or production process scheduling |
| Beschreibung: | 1 Online-Ressource (XII, 196 p) |
| ISBN: | 9781447107859 |
| DOI: | 10.1007/978-1-4471-0785-9 |
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Datensatz im Suchindex
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| author | Rovithakis, George A. Christodoulou, Manolis A. |
| author_facet | Rovithakis, George A. Christodoulou, Manolis A. |
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| dewey-full | 006.3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
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| dewey-sort | 16.3 |
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| discipline_str_mv | Informatik Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
| doi_str_mv | 10.1007/978-1-4471-0785-9 |
| edition | 1st ed. 2000 |
| format | Electronic eBook |
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| id | DE-604.BV047064130 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T16:12:22Z |
| indexdate | 2024-07-10T09:01:34Z |
| institution | BVB |
| isbn | 9781447107859 |
| language | English |
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| publisher | Springer London |
| record_format | marc |
| series2 | Advances in Industrial Control |
| spelling | Rovithakis, George A. Verfasser aut Adaptive Control with Recurrent High-order Neural Networks Theory and Industrial Applications by George A. Rovithakis, Manolis A. Christodoulou 1st ed. 2000 London Springer London 2000 1 Online-Ressource (XII, 196 p) txt rdacontent c rdamedia cr rdacarrier Advances in Industrial Control The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies ... , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. Neural networks is one of those areas where an initial burst of enthusiasm and optimism leads to an explosion of papers in the journals and many presentations at conferences but it is only in the last decade that significant theoretical work on stability, convergence and robustness for the use of neural networks in control systems has been tackled. George Rovithakis and Manolis Christodoulou have been interested in these theoretical problems and in the practical aspects of neural network applications to industrial problems. This very welcome addition to the Advances in Industrial Control series provides a succinct report of their research. The neural network model at the core of their work is the Recurrent High Order Neural Network (RHONN) and a complete theoretical and simulation development is presented. Different readers will find different aspects of the development of interest. The last chapter of the monograph discusses the problem of manufacturing or production process scheduling Artificial Intelligence Simulation and Modeling Control, Robotics, Mechatronics Complexity Artificial intelligence Computer simulation Control engineering Robotics Mechatronics Computational complexity Adaptivregelung (DE-588)4000457-0 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Adaptivregelung (DE-588)4000457-0 s Neuronales Netz (DE-588)4226127-2 s DE-604 Christodoulou, Manolis A. aut Erscheint auch als Druck-Ausgabe 9781447112013 Erscheint auch als Druck-Ausgabe 9781852336233 Erscheint auch als Druck-Ausgabe 9781447107866 https://doi.org/10.1007/978-1-4471-0785-9 Verlag URL des Eerstveröffentlichers Volltext |
| spellingShingle | Rovithakis, George A. Christodoulou, Manolis A. Adaptive Control with Recurrent High-order Neural Networks Theory and Industrial Applications Artificial Intelligence Simulation and Modeling Control, Robotics, Mechatronics Complexity Artificial intelligence Computer simulation Control engineering Robotics Mechatronics Computational complexity Adaptivregelung (DE-588)4000457-0 gnd Neuronales Netz (DE-588)4226127-2 gnd |
| subject_GND | (DE-588)4000457-0 (DE-588)4226127-2 |
| title | Adaptive Control with Recurrent High-order Neural Networks Theory and Industrial Applications |
| title_auth | Adaptive Control with Recurrent High-order Neural Networks Theory and Industrial Applications |
| title_exact_search | Adaptive Control with Recurrent High-order Neural Networks Theory and Industrial Applications |
| title_exact_search_txtP | Adaptive Control with Recurrent High-order Neural Networks Theory and Industrial Applications |
| title_full | Adaptive Control with Recurrent High-order Neural Networks Theory and Industrial Applications by George A. Rovithakis, Manolis A. Christodoulou |
| title_fullStr | Adaptive Control with Recurrent High-order Neural Networks Theory and Industrial Applications by George A. Rovithakis, Manolis A. Christodoulou |
| title_full_unstemmed | Adaptive Control with Recurrent High-order Neural Networks Theory and Industrial Applications by George A. Rovithakis, Manolis A. Christodoulou |
| title_short | Adaptive Control with Recurrent High-order Neural Networks |
| title_sort | adaptive control with recurrent high order neural networks theory and industrial applications |
| title_sub | Theory and Industrial Applications |
| topic | Artificial Intelligence Simulation and Modeling Control, Robotics, Mechatronics Complexity Artificial intelligence Computer simulation Control engineering Robotics Mechatronics Computational complexity Adaptivregelung (DE-588)4000457-0 gnd Neuronales Netz (DE-588)4226127-2 gnd |
| topic_facet | Artificial Intelligence Simulation and Modeling Control, Robotics, Mechatronics Complexity Artificial intelligence Computer simulation Control engineering Robotics Mechatronics Computational complexity Adaptivregelung Neuronales Netz |
| url | https://doi.org/10.1007/978-1-4471-0785-9 |
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