Fuzzy Logic, Identification and Predictive Control (eBook)

Jairo Jose Espinosa Oviedo, Joos P.L. Vandewalle, Vincent Wertz (Autoren)

eBook Download: PDF

2007 | 2005
XX, 264 Seiten
Springer London (Verlag)
978-1-84628-087-0 (ISBN)

Infos

Modern industrial processes and systems require adaptable advanced control protocols able to deal with circumstances demanding 'judgement' rather than simple 'yes/no', 'on/off' responses: circumstances where a linguistic description is often more relevant than a cut-and-dried numerical one. The ability of fuzzy systems to handle numeric and linguistic information within a single framework renders them efficacious for this purpose.

Fuzzy Logic, Identification and Predictive Control first shows you how to construct static and dynamic fuzzy models using the numerical data from a variety of real industrial systems and simulations. The second part exploits such models to design control systems employing techniques like data mining.

This monograph presents a combination of fuzzy control theory and industrial serviceability that will make a telling contribution to your research whether in the academic or industrial sphere and also serves as a fine roundup of the fuzzy control area for the graduate student.

Jairo Espinosa had a considerable experience of the practitioner side of advanced control systems and fuzzy systems in particular working with such companies as Zenith Data Systems in his native Colombia. There, he also won prizes for his academic work and for electronic design. He now works for IPCOS a company specialising in the design of advanced control systems for many process industries. This wil allow the author to draw on a good selection of industrial situations in writing the book.

 

Vincent Wertz is now head of the Automatic Control Group at Louvain where he is particularly active in Ph.D. supervision work (his contributions to the book will ensure relevance to the graduate market) and the book reflects all of his main research interests.

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Modern industrial processes and systems require adaptable advanced control protocols able to deal with circumstances demanding "e;judgement"e; rather than simple "e;yes/no"e;, "e;on/off"e; responses: circumstances where a linguistic description is often more relevant than a cut-and-dried numerical one. The ability of fuzzy systems to handle numeric and linguistic information within a single framework renders them efficacious for this purpose. Fuzzy Logic, Identification and Predictive Control first shows you how to construct static and dynamic fuzzy models using the numerical data from a variety of real industrial systems and simulations. The second part exploits such models to design control systems employing techniques like data mining. This monograph presents a combination of fuzzy control theory and industrial serviceability that will make a telling contribution to your research whether in the academic or industrial sphere and also serves as a fine roundup of the fuzzy control area for the graduate student.

Inhalt

Part I: Fuzzy Modelling Fuzzy Modeling Constructing Fuzzy Models from Input-Output Data Fuzzy Modelling with Linguistic Integrity: A Tool for Data Mining Nonlinear Identification Using Fuzzy Models Part II: Fuzzy Control Fuzzy Control Predictive Control Based on Fuzzy Models Robust Nonlinear Predictive Control Using Fuzzy Models Conclusions and Future Perspectives Part III: Appendices A. Fuzzy Set Theory B. Clustering Methods C. Gradients Used in Identification with Fuzzy Models D. Discrete Linear Dynamic Approximation Theorem E. Fuzzy Control for a CVT

Produkt-Details

• Erscheint lt. Verlag: 4.1.2007
• Reihe/Serie: Advances in Industrial Control
• Reihe/Serie: Advances in Industrial Control
• Zusatzinfo: XX, 264 p.
• Verlagsort: London
• Sprache: englisch
• Themenwelt: Informatik ► Datenbanken ► Data Warehouse / Data Mining
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
• Themenwelt: Naturwissenschaften
• Themenwelt: Technik ► Elektrotechnik / Energietechnik
• Schlagworte: Complexity • Control • Control Applications • control engineering • Data Mining • Fuzzy Control • Fuzzy Modelling • Intelligent Control • Modeling • Nonlinear Predictive Control • Simulation
• ISBN-10: 1-84628-087-7 / 1846280877
• ISBN-13: 978-1-84628-087-0 / 9781846280870