Fuzzy logic, identification and predictive control
Author(s)
Bibliographic Information
Fuzzy logic, identification and predictive control
（Advances in industrial control）
Springer, c2005
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Note
Includes bibliographical references (p. [255]-259) and index
Description and Table of Contents
Description
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.
Table of Contents
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
by "Nielsen BookData"