Intelligent control: aspects of fuzzy logic and neural nets
With increasing demands for high precision autonomous control over wide operating envelopes, conventional control engineering approaches are unable to adequately deal with system complexity, nonlinearities, spatial and temporal parameter variations, and with uncertainty. Intelligent Control or self-...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Singapore
World Scientific Pub. Co.
c1993
|
| Schriftenreihe: | World Scientific series in robotics and automated systems
vol. 6 |
| Schlagworte: | |
| Online-Zugang: | FHN01 Volltext |
| Zusammenfassung: | With increasing demands for high precision autonomous control over wide operating envelopes, conventional control engineering approaches are unable to adequately deal with system complexity, nonlinearities, spatial and temporal parameter variations, and with uncertainty. Intelligent Control or self-organising/learning control is a new emerging discipline that is designed to deal with problems. Rather than being model based, it is experiential based. Intelligent Control is the amalgam of the disciplines of Artificial Intelligence, Systems Theory and Operations Research. It uses most recent experiences or evidence to improve its performance through a variety of learning schemas, that for practical implementation must demonstrate rapid learning convergence, be temporally stable, be robust to parameter changes and internal and external disturbances. It is shown in this book that a wide class of fuzzy logic and neural net based learning algorithms satisfy these conditions. It is demonstrated that this class of intelligent controllers is based upon a fixed nonlinear mapping of the input (sensor) vector, followed by an output layer linear mapping with coefficients that are updated by various first order learning laws. Under these conditions self-organising fuzzy logic controllers and neural net controllers have common learning attributes.A theme example of the navigation and control of an autonomous guided vehicle is included throughout, together with a series of bench examples to demonstrate this new theory and its applicability |
| Beschreibung: | xvii, 380 p. ill |
| ISBN: | 9789814354738 |
Internformat
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| 520 | |a With increasing demands for high precision autonomous control over wide operating envelopes, conventional control engineering approaches are unable to adequately deal with system complexity, nonlinearities, spatial and temporal parameter variations, and with uncertainty. Intelligent Control or self-organising/learning control is a new emerging discipline that is designed to deal with problems. Rather than being model based, it is experiential based. Intelligent Control is the amalgam of the disciplines of Artificial Intelligence, Systems Theory and Operations Research. It uses most recent experiences or evidence to improve its performance through a variety of learning schemas, that for practical implementation must demonstrate rapid learning convergence, be temporally stable, be robust to parameter changes and internal and external disturbances. It is shown in this book that a wide class of fuzzy logic and neural net based learning algorithms satisfy these conditions. It is demonstrated that this class of intelligent controllers is based upon a fixed nonlinear mapping of the input (sensor) vector, followed by an output layer linear mapping with coefficients that are updated by various first order learning laws. Under these conditions self-organising fuzzy logic controllers and neural net controllers have common learning attributes.A theme example of the navigation and control of an autonomous guided vehicle is included throughout, together with a series of bench examples to demonstrate this new theory and its applicability | ||
| 650 | 4 | |a Intelligent control systems | |
| 650 | 4 | |a Fuzzy logic | |
| 650 | 4 | |a Neural networks (Computer science) | |
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Datensatz im Suchindex
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| author | Harris, C. J. |
| author_facet | Harris, C. J. |
| author_role | aut |
| author_sort | Harris, C. J. |
| author_variant | c j h cj cjh |
| building | Verbundindex |
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| dewey-full | 629.89 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 629 - Other branches of engineering |
| dewey-raw | 629.89 |
| dewey-search | 629.89 |
| dewey-sort | 3629.89 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Informatik Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
| format | Electronic eBook |
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| id | DE-604.BV044638559 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:57:53Z |
| institution | BVB |
| isbn | 9789814354738 |
| language | English |
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| physical | xvii, 380 p. ill |
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| spelling | Harris, C. J. Verfasser aut Intelligent control aspects of fuzzy logic and neural nets C.J. Harris, C.G. Moore & M. Brown Singapore World Scientific Pub. Co. c1993 xvii, 380 p. ill txt rdacontent c rdamedia cr rdacarrier World Scientific series in robotics and automated systems vol. 6 With increasing demands for high precision autonomous control over wide operating envelopes, conventional control engineering approaches are unable to adequately deal with system complexity, nonlinearities, spatial and temporal parameter variations, and with uncertainty. Intelligent Control or self-organising/learning control is a new emerging discipline that is designed to deal with problems. Rather than being model based, it is experiential based. Intelligent Control is the amalgam of the disciplines of Artificial Intelligence, Systems Theory and Operations Research. It uses most recent experiences or evidence to improve its performance through a variety of learning schemas, that for practical implementation must demonstrate rapid learning convergence, be temporally stable, be robust to parameter changes and internal and external disturbances. It is shown in this book that a wide class of fuzzy logic and neural net based learning algorithms satisfy these conditions. It is demonstrated that this class of intelligent controllers is based upon a fixed nonlinear mapping of the input (sensor) vector, followed by an output layer linear mapping with coefficients that are updated by various first order learning laws. Under these conditions self-organising fuzzy logic controllers and neural net controllers have common learning attributes.A theme example of the navigation and control of an autonomous guided vehicle is included throughout, together with a series of bench examples to demonstrate this new theory and its applicability Intelligent control systems Fuzzy logic Neural networks (Computer science) Kontrolle (DE-588)4032312-2 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Fuzzy-Logik (DE-588)4341284-1 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 s Fuzzy-Logik (DE-588)4341284-1 s Kontrolle (DE-588)4032312-2 s 1\p DE-604 Moore, C. G. Sonstige oth Brown, M. Sonstige oth Erscheint auch als Druck-Ausgabe 9810210426 http://www.worldscientific.com/worldscibooks/10.1142/1721#t=toc Verlag URL des Erstveroeffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Harris, C. J. Intelligent control aspects of fuzzy logic and neural nets Intelligent control systems Fuzzy logic Neural networks (Computer science) Kontrolle (DE-588)4032312-2 gnd Neuronales Netz (DE-588)4226127-2 gnd Fuzzy-Logik (DE-588)4341284-1 gnd |
| subject_GND | (DE-588)4032312-2 (DE-588)4226127-2 (DE-588)4341284-1 |
| title | Intelligent control aspects of fuzzy logic and neural nets |
| title_auth | Intelligent control aspects of fuzzy logic and neural nets |
| title_exact_search | Intelligent control aspects of fuzzy logic and neural nets |
| title_full | Intelligent control aspects of fuzzy logic and neural nets C.J. Harris, C.G. Moore & M. Brown |
| title_fullStr | Intelligent control aspects of fuzzy logic and neural nets C.J. Harris, C.G. Moore & M. Brown |
| title_full_unstemmed | Intelligent control aspects of fuzzy logic and neural nets C.J. Harris, C.G. Moore & M. Brown |
| title_short | Intelligent control |
| title_sort | intelligent control aspects of fuzzy logic and neural nets |
| title_sub | aspects of fuzzy logic and neural nets |
| topic | Intelligent control systems Fuzzy logic Neural networks (Computer science) Kontrolle (DE-588)4032312-2 gnd Neuronales Netz (DE-588)4226127-2 gnd Fuzzy-Logik (DE-588)4341284-1 gnd |
| topic_facet | Intelligent control systems Fuzzy logic Neural networks (Computer science) Kontrolle Neuronales Netz Fuzzy-Logik |
| url | http://www.worldscientific.com/worldscibooks/10.1142/1721#t=toc |
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