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Fuzzy logic, identification, and predictive control

Author: Jairo Espinosa; J Vandewalle; Vincent Wertz
Publisher: London ; New York : Springer, 2004.
Series: Advances in industrial control.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
"Fuzzy Logic, Identification and Predictive Control is a comprehensive introduction to the use of fuzzy methods in many different control paradigms encompassing robust, model-based, PID-like and predictive control. This combination of fuzzy control theory and industrial serviceability will make a telling contribution to your research whether in the academic or industrial sphere and also serves as a fine roundup of  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Espinosa, Jairo.
Fuzzy logic, identification, and predictive control.
London ; New York : Springer, 2004
(DLC) 2004046864
(OCoLC)55104841
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Jairo Espinosa; J Vandewalle; Vincent Wertz
ISBN: 1852338288 9781852338282 1846280877 9781846280870
OCLC Number: 62776356
Description: 1 online resource (xix, 263 pages) : illustrations.
Contents: Cover --
Preface --
Table of Contents --
Part I Fuzzy Modeling --
1 Fuzzy Modeling. --
1.1 Function Approximation . --
1.2 Approximation Capabilities of Takagi ... Sugeno Fuzzy Models --
1.3 Conclusion and Summary --
2 Constructing Fuzzy Models from Input-Output Data --
2.1 Mosaic or Table Lookup Scheme. --
2.2 Using Gradient Descent --
2.3 Using Clustering and Gradient Descent --
2.4 Using Evolutionary Strategies --
2.5 Generalization and Consequences Estimation. --
2.6 Example of an Industrial Application --
2.7 Conclusions --
3 Fuzzy Modeling with Linguistic Integrity: A Tool for Data Mining --
3.1 Introduction --
3.2 Structure of the Fuzzy Model --
3.3 The AFRELI Algorithm. --
3.4 The FuZion Algorithm --
3.5 Examples. --
3.6 Complexity of the AFRELI Algorithm --
3.7 Conclusions --
4 Nonlinear Identification Using Fuzzy Models --
4.1 System Identification --
4.2 Basic Structure of the Fuzzy System --
4.3 Experiment Design for System Identification --
4.4 Choosing the Regressors --
4.5 Choosing the Structure. --
4.6 Calculating the Parameters --
4.7 Validation --
4.8 Example: Identification of the Box and Jenkins Gas Furnace Data Set --
4.9 Identification of Takagi ... Sugeno Fuzzy Models Using Local Linear Identification --
4.10 Conclusions --
Part II Fuzzy Control --
5 Fuzzy Control --
5.1 Model-Free Fuzzy Control --
5.2 Model Based Fuzzy Control --
5.3 Conclusions and Future Perspectives --
6 Predictive Control Based on Fuzzy Models --
6.1 The Predictive Control Strategy --
6.2 Unconstrained Nonlinear Predictive Control --
6.3 Constrained Nonlinear Predictive Control --
6.4 Conclusions --
7 Robust Nonlinear Predictive Control Using Fuzzy Models --
7.1 Introduction --
7.2 Robust Quadratic Programming --
7.3 Problem Description --
7.4 Nominal Solution --
7.5 Formulation of the MPC Problem as a Robust QP. --
7.6 The Control Algorithm. --
7.7 Uncertainty Description in Fuzzy Models --
7.8 Conclusions and Perspectives --
8 Conclusions and Future Perspectives --
8.1 Conclusions and Summary --
8.2 Perspectives and Future Work. --
Part III Appendices --
A Fuzzy Set Theory --
B Clustering Methods --
C Gradients Used in Identification with Fuzzy Models --
D Discrete Linear Dynamical System Approximation Theorem. --
E Fuzzy Control for a Continuously Variable Transmission --
References.
Series Title: Advances in industrial control.
Responsibility: Jairo Espinosa, Joos Vandewalle, Vincent Wertz.
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Abstract:

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:  Read more...

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From the reviews:"New insights into the transfer of fuzzy methods into the modern control paradigms encompassing robust, model-based, PID-like, and predictive control are presented in this book. ... Read more...

 
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