Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models
The goal of this book is to provide engineers and scientIsts in academia and industry with a thorough understanding of the underlying principles of nonlinear system identification. The reader will be able to apply the discussed models and methods to real problems with the necessary confidence and th...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Berlin, Heidelberg
Springer Berlin Heidelberg
2001
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| Schlagwörter: | |
| Links: | https://doi.org/10.1007/978-3-662-04323-3 https://doi.org/10.1007/978-3-662-04323-3 https://doi.org/10.1007/978-3-662-04323-3 |
| Zusammenfassung: | The goal of this book is to provide engineers and scientIsts in academia and industry with a thorough understanding of the underlying principles of nonlinear system identification. The reader will be able to apply the discussed models and methods to real problems with the necessary confidence and the awareness of potential difficulties that may arise in practice. This book is self-contained in the sense that it requires merely basic knowledge of matrix algebra, signals and systems, and statistics. Therefore, it also serves as an introduction to linear system identification and gives a practical overview on the major optimization methods used in engineering. The emphasis of this book is on an intuitive understanding of the subject and the practical application of the discussed techniques. It is not written in a theorem/proof style; rather the mathematics is kept to a minimum and the pursued ideas are illustrated by numerous figures, examples, and real-world applications. Fifteen years ago, nonlinear system identification was a field of several ad-hoc approaches, each applicable only to a very restricted class of systems. With the advent of neural networks, fuzzy models, and modern structure opti mization techniques a much wider class of systems can be handled. Although one major characteristic of nonlinear systems is that almost every nonlinear system is unique, tools have been developed that allow the use of the same ap proach for a broad variety of systems |
| Umfang: | 1 Online-Ressource (XVII, 786 p) |
| ISBN: | 9783662043233 |
| DOI: | 10.1007/978-3-662-04323-3 |
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| 520 | |a The goal of this book is to provide engineers and scientIsts in academia and industry with a thorough understanding of the underlying principles of nonlinear system identification. The reader will be able to apply the discussed models and methods to real problems with the necessary confidence and the awareness of potential difficulties that may arise in practice. This book is self-contained in the sense that it requires merely basic knowledge of matrix algebra, signals and systems, and statistics. Therefore, it also serves as an introduction to linear system identification and gives a practical overview on the major optimization methods used in engineering. The emphasis of this book is on an intuitive understanding of the subject and the practical application of the discussed techniques. It is not written in a theorem/proof style; rather the mathematics is kept to a minimum and the pursued ideas are illustrated by numerous figures, examples, and real-world applications. Fifteen years ago, nonlinear system identification was a field of several ad-hoc approaches, each applicable only to a very restricted class of systems. With the advent of neural networks, fuzzy models, and modern structure opti mization techniques a much wider class of systems can be handled. Although one major characteristic of nonlinear systems is that almost every nonlinear system is unique, tools have been developed that allow the use of the same ap proach for a broad variety of systems | ||
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| illustrated | Not Illustrated |
| indexdate | 2024-12-20T18:19:20Z |
| institution | BVB |
| isbn | 9783662043233 |
| language | English |
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| physical | 1 Online-Ressource (XVII, 786 p) |
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| publisher | Springer Berlin Heidelberg |
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| spelling | Nelles, Oliver Verfasser aut Nonlinear System Identification From Classical Approaches to Neural Networks and Fuzzy Models by Oliver Nelles Berlin, Heidelberg Springer Berlin Heidelberg 2001 1 Online-Ressource (XVII, 786 p) txt rdacontent c rdamedia cr rdacarrier The goal of this book is to provide engineers and scientIsts in academia and industry with a thorough understanding of the underlying principles of nonlinear system identification. The reader will be able to apply the discussed models and methods to real problems with the necessary confidence and the awareness of potential difficulties that may arise in practice. This book is self-contained in the sense that it requires merely basic knowledge of matrix algebra, signals and systems, and statistics. Therefore, it also serves as an introduction to linear system identification and gives a practical overview on the major optimization methods used in engineering. The emphasis of this book is on an intuitive understanding of the subject and the practical application of the discussed techniques. It is not written in a theorem/proof style; rather the mathematics is kept to a minimum and the pursued ideas are illustrated by numerous figures, examples, and real-world applications. Fifteen years ago, nonlinear system identification was a field of several ad-hoc approaches, each applicable only to a very restricted class of systems. With the advent of neural networks, fuzzy models, and modern structure opti mization techniques a much wider class of systems can be handled. Although one major characteristic of nonlinear systems is that almost every nonlinear system is unique, tools have been developed that allow the use of the same ap proach for a broad variety of systems Engineering Control Control, Robotics, Mechatronics Complexity Calculus of Variations and Optimal Control; Optimization Simulation and Modeling Computer simulation Calculus of variations Complexity, Computational Control engineering Robotics Mechatronics Nichtlineares dynamisches System (DE-588)4126142-2 gnd rswk-swf Systemidentifikation (DE-588)4121753-6 gnd rswk-swf Nichtlineares dynamisches System (DE-588)4126142-2 s Systemidentifikation (DE-588)4121753-6 s 1\p DE-604 Erscheint auch als Druck-Ausgabe 9783642086748 https://doi.org/10.1007/978-3-662-04323-3 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Nelles, Oliver Nonlinear System Identification From Classical Approaches to Neural Networks and Fuzzy Models Engineering Control Control, Robotics, Mechatronics Complexity Calculus of Variations and Optimal Control; Optimization Simulation and Modeling Computer simulation Calculus of variations Complexity, Computational Control engineering Robotics Mechatronics Nichtlineares dynamisches System (DE-588)4126142-2 gnd Systemidentifikation (DE-588)4121753-6 gnd |
| subject_GND | (DE-588)4126142-2 (DE-588)4121753-6 |
| title | Nonlinear System Identification From Classical Approaches to Neural Networks and Fuzzy Models |
| title_auth | Nonlinear System Identification From Classical Approaches to Neural Networks and Fuzzy Models |
| title_exact_search | Nonlinear System Identification From Classical Approaches to Neural Networks and Fuzzy Models |
| title_full | Nonlinear System Identification From Classical Approaches to Neural Networks and Fuzzy Models by Oliver Nelles |
| title_fullStr | Nonlinear System Identification From Classical Approaches to Neural Networks and Fuzzy Models by Oliver Nelles |
| title_full_unstemmed | Nonlinear System Identification From Classical Approaches to Neural Networks and Fuzzy Models by Oliver Nelles |
| title_short | Nonlinear System Identification |
| title_sort | nonlinear system identification from classical approaches to neural networks and fuzzy models |
| title_sub | From Classical Approaches to Neural Networks and Fuzzy Models |
| topic | Engineering Control Control, Robotics, Mechatronics Complexity Calculus of Variations and Optimal Control; Optimization Simulation and Modeling Computer simulation Calculus of variations Complexity, Computational Control engineering Robotics Mechatronics Nichtlineares dynamisches System (DE-588)4126142-2 gnd Systemidentifikation (DE-588)4121753-6 gnd |
| topic_facet | Engineering Control Control, Robotics, Mechatronics Complexity Calculus of Variations and Optimal Control; Optimization Simulation and Modeling Computer simulation Calculus of variations Complexity, Computational Control engineering Robotics Mechatronics Nichtlineares dynamisches System Systemidentifikation |
| url | https://doi.org/10.1007/978-3-662-04323-3 |
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