Fuzzy Control of Industrial Systems: Theory and Applications
Fuzzy Control of Industrial Systems: Theory and Applications presents the basic theoretical framework of crisp and fuzzy set theory, relating these concepts to control engineering based on the analogy between the Laplace transfer function of linear systems and the fuzzy relation of a nonlinear fuzzy...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
1998
|
| Schriftenreihe: | The Springer International Series in Engineering and Computer Science
457 |
| Schlagworte: | |
| Online-Zugang: | BTU01 URL des Erstveröffentlichers |
| Zusammenfassung: | Fuzzy Control of Industrial Systems: Theory and Applications presents the basic theoretical framework of crisp and fuzzy set theory, relating these concepts to control engineering based on the analogy between the Laplace transfer function of linear systems and the fuzzy relation of a nonlinear fuzzy system. Included are generic aspects of fuzzy systems with an emphasis on the many degrees of freedom and its practical design implications, modeling and systems identification techniques based on fuzzy rules, parametrized rules and relational equations, and the principles of adaptive fuzzy and neurofuzzy systems. Practical design aspects of fuzzy controllers are covered by the detailed treatment of fuzzy and neurofuzzy software design tools with an emphasis on iterative fuzzy tuning, while novel stability limit testing methods and the definition and practical examples of the new concept of collaborative control systems are also given. In addition, case studies of successful applications in industrial automation, process control, electric power technology, electric traction, traffic engineering, wastewater treatment, manufacturing, mineral processing and automotive engineering are also presented, in order to assist industrial control systems engineers in recognizing situations when fuzzy and neurofuzzy would offer certain advantages over traditional methods, particularly in controlling highly nonlinear and time-variant plants and processes |
| Beschreibung: | 1 Online-Ressource (XXIII, 192 p) |
| ISBN: | 9781475728132 |
| DOI: | 10.1007/978-1-4757-2813-2 |
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| 520 | |a Fuzzy Control of Industrial Systems: Theory and Applications presents the basic theoretical framework of crisp and fuzzy set theory, relating these concepts to control engineering based on the analogy between the Laplace transfer function of linear systems and the fuzzy relation of a nonlinear fuzzy system. Included are generic aspects of fuzzy systems with an emphasis on the many degrees of freedom and its practical design implications, modeling and systems identification techniques based on fuzzy rules, parametrized rules and relational equations, and the principles of adaptive fuzzy and neurofuzzy systems. Practical design aspects of fuzzy controllers are covered by the detailed treatment of fuzzy and neurofuzzy software design tools with an emphasis on iterative fuzzy tuning, while novel stability limit testing methods and the definition and practical examples of the new concept of collaborative control systems are also given. In addition, case studies of successful applications in industrial automation, process control, electric power technology, electric traction, traffic engineering, wastewater treatment, manufacturing, mineral processing and automotive engineering are also presented, in order to assist industrial control systems engineers in recognizing situations when fuzzy and neurofuzzy would offer certain advantages over traditional methods, particularly in controlling highly nonlinear and time-variant plants and processes | ||
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Datensatz im Suchindex
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| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 511 - General principles of mathematics |
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| dewey-search | 511.3 |
| dewey-sort | 3511.3 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| doi_str_mv | 10.1007/978-1-4757-2813-2 |
| format | Electronic eBook |
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| id | DE-604.BV045185236 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:10:55Z |
| institution | BVB |
| isbn | 9781475728132 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030574414 |
| oclc_num | 1053826341 |
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| physical | 1 Online-Ressource (XXIII, 192 p) |
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| publishDate | 1998 |
| publishDateSearch | 1998 |
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| publisher | Springer US |
| record_format | marc |
| series2 | The Springer International Series in Engineering and Computer Science |
| spelling | Shaw, Ian S. Verfasser aut Fuzzy Control of Industrial Systems Theory and Applications by Ian S. Shaw Boston, MA Springer US 1998 1 Online-Ressource (XXIII, 192 p) txt rdacontent c rdamedia cr rdacarrier The Springer International Series in Engineering and Computer Science 457 Fuzzy Control of Industrial Systems: Theory and Applications presents the basic theoretical framework of crisp and fuzzy set theory, relating these concepts to control engineering based on the analogy between the Laplace transfer function of linear systems and the fuzzy relation of a nonlinear fuzzy system. Included are generic aspects of fuzzy systems with an emphasis on the many degrees of freedom and its practical design implications, modeling and systems identification techniques based on fuzzy rules, parametrized rules and relational equations, and the principles of adaptive fuzzy and neurofuzzy systems. Practical design aspects of fuzzy controllers are covered by the detailed treatment of fuzzy and neurofuzzy software design tools with an emphasis on iterative fuzzy tuning, while novel stability limit testing methods and the definition and practical examples of the new concept of collaborative control systems are also given. In addition, case studies of successful applications in industrial automation, process control, electric power technology, electric traction, traffic engineering, wastewater treatment, manufacturing, mineral processing and automotive engineering are also presented, in order to assist industrial control systems engineers in recognizing situations when fuzzy and neurofuzzy would offer certain advantages over traditional methods, particularly in controlling highly nonlinear and time-variant plants and processes Mathematics Mathematical Logic and Foundations Mechanical Engineering Electrical Engineering Mathematical logic Mechanical engineering Electrical engineering Fuzzy-Regelung (DE-588)4395755-9 gnd rswk-swf Fuzzy-Regelung (DE-588)4395755-9 s 1\p DE-604 Erscheint auch als Druck-Ausgabe 9781441950550 https://doi.org/10.1007/978-1-4757-2813-2 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Shaw, Ian S. Fuzzy Control of Industrial Systems Theory and Applications Mathematics Mathematical Logic and Foundations Mechanical Engineering Electrical Engineering Mathematical logic Mechanical engineering Electrical engineering Fuzzy-Regelung (DE-588)4395755-9 gnd |
| subject_GND | (DE-588)4395755-9 |
| title | Fuzzy Control of Industrial Systems Theory and Applications |
| title_auth | Fuzzy Control of Industrial Systems Theory and Applications |
| title_exact_search | Fuzzy Control of Industrial Systems Theory and Applications |
| title_full | Fuzzy Control of Industrial Systems Theory and Applications by Ian S. Shaw |
| title_fullStr | Fuzzy Control of Industrial Systems Theory and Applications by Ian S. Shaw |
| title_full_unstemmed | Fuzzy Control of Industrial Systems Theory and Applications by Ian S. Shaw |
| title_short | Fuzzy Control of Industrial Systems |
| title_sort | fuzzy control of industrial systems theory and applications |
| title_sub | Theory and Applications |
| topic | Mathematics Mathematical Logic and Foundations Mechanical Engineering Electrical Engineering Mathematical logic Mechanical engineering Electrical engineering Fuzzy-Regelung (DE-588)4395755-9 gnd |
| topic_facet | Mathematics Mathematical Logic and Foundations Mechanical Engineering Electrical Engineering Mathematical logic Mechanical engineering Electrical engineering Fuzzy-Regelung |
| url | https://doi.org/10.1007/978-1-4757-2813-2 |
| work_keys_str_mv | AT shawians fuzzycontrolofindustrialsystemstheoryandapplications |