Neural Networks for Identification, Prediction and Control:
In recent years, there has been a growing interest in applying neural networks to dynamic systems identification (modelling), prediction and control. Neural networks are computing systems characterised by the ability to learn from examples rather than having to be programmed in a conventional sense....
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
London
Springer London
1995
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| Schlagworte: | |
| Online-Zugang: | BTU01 Volltext |
| Zusammenfassung: | In recent years, there has been a growing interest in applying neural networks to dynamic systems identification (modelling), prediction and control. Neural networks are computing systems characterised by the ability to learn from examples rather than having to be programmed in a conventional sense. Their use enables the behaviour of complex systems to be modelled and predicted and accurate control to be achieved through training, without a priori information about the systems' structures or parameters. This book describes examples of applications of neural networks In modelling, prediction and control. The topics covered include identification of general linear and non-linear processes, forecasting of river levels, stock market prices and currency exchange rates, and control of a time-delayed plant and a two-joint robot. These applications employ the major types of neural networks and learning algorithms. The neural network types considered in detail are the muhilayer perceptron (MLP), the Elman and Jordan networks and the Group-Method-of-Data-Handling (GMDH) network. In addition, cerebellar-model-articulation-controller (CMAC) networks and neuromorphic fuzzy logic systems are also presented. The main learning algorithm adopted in the applications is the standard backpropagation (BP) algorithm. Widrow-Hoff learning, dynamic BP and evolutionary learning are also described |
| Beschreibung: | 1 Online-Ressource (XIV, 238 p) |
| ISBN: | 9781447132448 |
| DOI: | 10.1007/978-1-4471-3244-8 |
Internformat
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| 520 | |a In recent years, there has been a growing interest in applying neural networks to dynamic systems identification (modelling), prediction and control. Neural networks are computing systems characterised by the ability to learn from examples rather than having to be programmed in a conventional sense. Their use enables the behaviour of complex systems to be modelled and predicted and accurate control to be achieved through training, without a priori information about the systems' structures or parameters. This book describes examples of applications of neural networks In modelling, prediction and control. The topics covered include identification of general linear and non-linear processes, forecasting of river levels, stock market prices and currency exchange rates, and control of a time-delayed plant and a two-joint robot. These applications employ the major types of neural networks and learning algorithms. The neural network types considered in detail are the muhilayer perceptron (MLP), the Elman and Jordan networks and the Group-Method-of-Data-Handling (GMDH) network. In addition, cerebellar-model-articulation-controller (CMAC) networks and neuromorphic fuzzy logic systems are also presented. The main learning algorithm adopted in the applications is the standard backpropagation (BP) algorithm. Widrow-Hoff learning, dynamic BP and evolutionary learning are also described | ||
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Datensatz im Suchindex
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| any_adam_object | |
| author | Pham, Duc Truong Liu, Xing |
| author_facet | Pham, Duc Truong Liu, Xing |
| author_role | aut aut |
| author_sort | Pham, Duc Truong |
| author_variant | d t p dt dtp x l xl |
| building | Verbundindex |
| bvnumber | BV045186101 |
| collection | ZDB-2-ENG |
| ctrlnum | (ZDB-2-ENG)978-1-4471-3244-8 (OCoLC)1184494867 (DE-599)BVBBV045186101 |
| dewey-full | 629.8 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 629 - Other branches of engineering |
| dewey-raw | 629.8 |
| dewey-search | 629.8 |
| dewey-sort | 3629.8 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
| doi_str_mv | 10.1007/978-1-4471-3244-8 |
| format | Electronic eBook |
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| id | DE-604.BV045186101 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:10:56Z |
| institution | BVB |
| isbn | 9781447132448 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030575277 |
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| physical | 1 Online-Ressource (XIV, 238 p) |
| psigel | ZDB-2-ENG ZDB-2-ENG_Archiv ZDB-2-ENG ZDB-2-ENG_Archiv |
| publishDate | 1995 |
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| publishDateSort | 1995 |
| publisher | Springer London |
| record_format | marc |
| spelling | Pham, Duc Truong Verfasser aut Neural Networks for Identification, Prediction and Control by Duc Truong Pham, Xing Liu London Springer London 1995 1 Online-Ressource (XIV, 238 p) txt rdacontent c rdamedia cr rdacarrier In recent years, there has been a growing interest in applying neural networks to dynamic systems identification (modelling), prediction and control. Neural networks are computing systems characterised by the ability to learn from examples rather than having to be programmed in a conventional sense. Their use enables the behaviour of complex systems to be modelled and predicted and accurate control to be achieved through training, without a priori information about the systems' structures or parameters. This book describes examples of applications of neural networks In modelling, prediction and control. The topics covered include identification of general linear and non-linear processes, forecasting of river levels, stock market prices and currency exchange rates, and control of a time-delayed plant and a two-joint robot. These applications employ the major types of neural networks and learning algorithms. The neural network types considered in detail are the muhilayer perceptron (MLP), the Elman and Jordan networks and the Group-Method-of-Data-Handling (GMDH) network. In addition, cerebellar-model-articulation-controller (CMAC) networks and neuromorphic fuzzy logic systems are also presented. The main learning algorithm adopted in the applications is the standard backpropagation (BP) algorithm. Widrow-Hoff learning, dynamic BP and evolutionary learning are also described Engineering Control Computational Intelligence Statistical Physics, Dynamical Systems and Complexity Complexity Pattern Recognition Pattern recognition Statistical physics Dynamical systems Computational intelligence Complexity, Computational Control engineering Zukunft (DE-588)4068097-6 gnd rswk-swf Identifikation (DE-588)4072712-9 gnd rswk-swf Regler (DE-588)4140088-4 gnd rswk-swf Systemidentifikation (DE-588)4121753-6 gnd rswk-swf Kontrollsystem (DE-588)4126040-5 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Prädiktive Regelung (DE-588)4271836-3 gnd rswk-swf Prädiktive Regelung (DE-588)4271836-3 s Neuronales Netz (DE-588)4226127-2 s 1\p DE-604 Systemidentifikation (DE-588)4121753-6 s 2\p DE-604 Regler (DE-588)4140088-4 s 3\p DE-604 Kontrollsystem (DE-588)4126040-5 s 4\p DE-604 Zukunft (DE-588)4068097-6 s 5\p DE-604 Identifikation (DE-588)4072712-9 s 6\p DE-604 Liu, Xing aut Erscheint auch als Druck-Ausgabe 9781447132462 https://doi.org/10.1007/978-1-4471-3244-8 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 4\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 5\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 6\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Pham, Duc Truong Liu, Xing Neural Networks for Identification, Prediction and Control Engineering Control Computational Intelligence Statistical Physics, Dynamical Systems and Complexity Complexity Pattern Recognition Pattern recognition Statistical physics Dynamical systems Computational intelligence Complexity, Computational Control engineering Zukunft (DE-588)4068097-6 gnd Identifikation (DE-588)4072712-9 gnd Regler (DE-588)4140088-4 gnd Systemidentifikation (DE-588)4121753-6 gnd Kontrollsystem (DE-588)4126040-5 gnd Neuronales Netz (DE-588)4226127-2 gnd Prädiktive Regelung (DE-588)4271836-3 gnd |
| subject_GND | (DE-588)4068097-6 (DE-588)4072712-9 (DE-588)4140088-4 (DE-588)4121753-6 (DE-588)4126040-5 (DE-588)4226127-2 (DE-588)4271836-3 |
| title | Neural Networks for Identification, Prediction and Control |
| title_auth | Neural Networks for Identification, Prediction and Control |
| title_exact_search | Neural Networks for Identification, Prediction and Control |
| title_full | Neural Networks for Identification, Prediction and Control by Duc Truong Pham, Xing Liu |
| title_fullStr | Neural Networks for Identification, Prediction and Control by Duc Truong Pham, Xing Liu |
| title_full_unstemmed | Neural Networks for Identification, Prediction and Control by Duc Truong Pham, Xing Liu |
| title_short | Neural Networks for Identification, Prediction and Control |
| title_sort | neural networks for identification prediction and control |
| topic | Engineering Control Computational Intelligence Statistical Physics, Dynamical Systems and Complexity Complexity Pattern Recognition Pattern recognition Statistical physics Dynamical systems Computational intelligence Complexity, Computational Control engineering Zukunft (DE-588)4068097-6 gnd Identifikation (DE-588)4072712-9 gnd Regler (DE-588)4140088-4 gnd Systemidentifikation (DE-588)4121753-6 gnd Kontrollsystem (DE-588)4126040-5 gnd Neuronales Netz (DE-588)4226127-2 gnd Prädiktive Regelung (DE-588)4271836-3 gnd |
| topic_facet | Engineering Control Computational Intelligence Statistical Physics, Dynamical Systems and Complexity Complexity Pattern Recognition Pattern recognition Statistical physics Dynamical systems Computational intelligence Complexity, Computational Control engineering Zukunft Identifikation Regler Systemidentifikation Kontrollsystem Neuronales Netz Prädiktive Regelung |
| url | https://doi.org/10.1007/978-1-4471-3244-8 |
| work_keys_str_mv | AT phamductruong neuralnetworksforidentificationpredictionandcontrol AT liuxing neuralnetworksforidentificationpredictionandcontrol |