Distributed Fuzzy Control of Multivariable Systems:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Dordrecht
Springer Netherlands
1996
|
| Schriftenreihe: | International Series in Intelligent Technologies
6 |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | It is known that many control processes are characterized by both quantitative and qualitative complexity. Tbe quantitative complexity is usually expressed in a large number of state variables, respectively high dimensional mathematical model. Tbe qualitative complexity is usually associated with uncertain behaviour, respectively approximately known mathematical model. If the above two aspects of complexity are considered separately, the corresponding control problem can be easily solved. On one hand, large scale systems theory has existed for more than 20 years and has proved its capabilities in solving high dimensional control problems on the basis of decomposition, hierarchy, decentralization and multilayers. On the other hand, the fuzzy linguistic approach is almost at the same age and has shown its advantages in solving approximately formulated control problems on the basis of linguistic reasoning and logical inference. However, if both aspects of complexity are considered together, the corresponding control problem becomes non-trivial and does not have an easy solution. Modem control theory and practice have reacted accordingly to the above mentioned new cballenges of tbe day by utilizing the latest achievements in computer technology and artificial intelligence distributed computation and intelligent operation. In this respect, a new field has emerged in the last decade, called " Distributed intelligent control systems" . However, the majority of the familiar works in this field are still either on an empirical or on a conceptual level and this is a significant drawback |
| Beschreibung: | 1 Online-Ressource (XIV, 186 p) |
| ISBN: | 9789401586405 9789048146529 |
| ISSN: | 1382-3434 |
| DOI: | 10.1007/978-94-015-8640-5 |
Internformat
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| 500 | |a It is known that many control processes are characterized by both quantitative and qualitative complexity. Tbe quantitative complexity is usually expressed in a large number of state variables, respectively high dimensional mathematical model. Tbe qualitative complexity is usually associated with uncertain behaviour, respectively approximately known mathematical model. If the above two aspects of complexity are considered separately, the corresponding control problem can be easily solved. On one hand, large scale systems theory has existed for more than 20 years and has proved its capabilities in solving high dimensional control problems on the basis of decomposition, hierarchy, decentralization and multilayers. On the other hand, the fuzzy linguistic approach is almost at the same age and has shown its advantages in solving approximately formulated control problems on the basis of linguistic reasoning and logical inference. However, if both aspects of complexity are considered together, the corresponding control problem becomes non-trivial and does not have an easy solution. Modem control theory and practice have reacted accordingly to the above mentioned new cballenges of tbe day by utilizing the latest achievements in computer technology and artificial intelligence distributed computation and intelligent operation. In this respect, a new field has emerged in the last decade, called " Distributed intelligent control systems" . However, the majority of the familiar works in this field are still either on an empirical or on a conceptual level and this is a significant drawback | ||
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Datensatz im Suchindex
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| isbn | 9789401586405 9789048146529 |
| issn | 1382-3434 |
| language | English |
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| spelling | Gegov, Alexander Verfasser aut Distributed Fuzzy Control of Multivariable Systems by Alexander Gegov Dordrecht Springer Netherlands 1996 1 Online-Ressource (XIV, 186 p) txt rdacontent c rdamedia cr rdacarrier International Series in Intelligent Technologies 6 1382-3434 It is known that many control processes are characterized by both quantitative and qualitative complexity. Tbe quantitative complexity is usually expressed in a large number of state variables, respectively high dimensional mathematical model. Tbe qualitative complexity is usually associated with uncertain behaviour, respectively approximately known mathematical model. If the above two aspects of complexity are considered separately, the corresponding control problem can be easily solved. On one hand, large scale systems theory has existed for more than 20 years and has proved its capabilities in solving high dimensional control problems on the basis of decomposition, hierarchy, decentralization and multilayers. On the other hand, the fuzzy linguistic approach is almost at the same age and has shown its advantages in solving approximately formulated control problems on the basis of linguistic reasoning and logical inference. However, if both aspects of complexity are considered together, the corresponding control problem becomes non-trivial and does not have an easy solution. Modem control theory and practice have reacted accordingly to the above mentioned new cballenges of tbe day by utilizing the latest achievements in computer technology and artificial intelligence distributed computation and intelligent operation. In this respect, a new field has emerged in the last decade, called " Distributed intelligent control systems" . However, the majority of the familiar works in this field are still either on an empirical or on a conceptual level and this is a significant drawback Mathematics Systems theory Logic, Symbolic and mathematical Mechanical engineering Computer engineering Mathematical Logic and Foundations Systems Theory, Control Electrical Engineering Mechanical Engineering Mathematik Fuzzy-Regelung (DE-588)4395755-9 gnd rswk-swf Kontrolltheorie (DE-588)4032317-1 gnd rswk-swf Fuzzy-Regelung (DE-588)4395755-9 s Kontrolltheorie (DE-588)4032317-1 s 1\p DE-604 https://doi.org/10.1007/978-94-015-8640-5 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Gegov, Alexander Distributed Fuzzy Control of Multivariable Systems Mathematics Systems theory Logic, Symbolic and mathematical Mechanical engineering Computer engineering Mathematical Logic and Foundations Systems Theory, Control Electrical Engineering Mechanical Engineering Mathematik Fuzzy-Regelung (DE-588)4395755-9 gnd Kontrolltheorie (DE-588)4032317-1 gnd |
| subject_GND | (DE-588)4395755-9 (DE-588)4032317-1 |
| title | Distributed Fuzzy Control of Multivariable Systems |
| title_auth | Distributed Fuzzy Control of Multivariable Systems |
| title_exact_search | Distributed Fuzzy Control of Multivariable Systems |
| title_full | Distributed Fuzzy Control of Multivariable Systems by Alexander Gegov |
| title_fullStr | Distributed Fuzzy Control of Multivariable Systems by Alexander Gegov |
| title_full_unstemmed | Distributed Fuzzy Control of Multivariable Systems by Alexander Gegov |
| title_short | Distributed Fuzzy Control of Multivariable Systems |
| title_sort | distributed fuzzy control of multivariable systems |
| topic | Mathematics Systems theory Logic, Symbolic and mathematical Mechanical engineering Computer engineering Mathematical Logic and Foundations Systems Theory, Control Electrical Engineering Mechanical Engineering Mathematik Fuzzy-Regelung (DE-588)4395755-9 gnd Kontrolltheorie (DE-588)4032317-1 gnd |
| topic_facet | Mathematics Systems theory Logic, Symbolic and mathematical Mechanical engineering Computer engineering Mathematical Logic and Foundations Systems Theory, Control Electrical Engineering Mechanical Engineering Mathematik Fuzzy-Regelung Kontrolltheorie |
| url | https://doi.org/10.1007/978-94-015-8640-5 |
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