Fuzzy Logic, Identification and Predictive Control (Advances in Industrial Control)

The complexity and sensitivity of modern industrial processes and systems increasingly require adaptable advanced control protocols. These controllers have to be able to deal with circumstances demanding "judgement" rather than simple "yes/no", "on/off" responses, circumstances where an imprecise 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 in this form of expert control system. Divided into two parts, __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-world industrial systems and simulations. The second part demonstrates the exploitation of such models to design control systems employing techniques like data mining. __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 the fuzzy control area for the graduate student. **Advances in Industrial Control** aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

the Complexity And Sensitivity Of Modern Industrial Processes And Systems Increasingly Require Adaptable Advanced Control Protocols. These Controllers Have To Be Able To Deal With Circumstances Demanding Judgement Rather Than Simple Yes/no, On/off Responses, Circumstances Where An Imprecise 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 In This Form Of Expert Control System.

divided Into Two Parts, 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-world Industrial Systems And Simulations. The Second Part Demonstrates The Exploitation Of Such Models To Design Control Systems Employing Techniques Like Data Mining.

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 The Fuzzy Control Area For The Graduate Student.

advances In Industrial Control Aims To Report And Encourage The Transfer Of Technology In Control Engineering. The Rapid Development Of Control Technology Has An Impact On All Areas Of The Control Discipline. The Series Offers An Opportunity For Researchers To Present An Extended Exposition Of New Workin All Aspects Of Industrial Control.

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. "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 the fuzzy control area for the graduate student."--BOOK JACKET Fuzzy Modeling....Pages 3-20 Constructing Fuzzy Models from Input-Output Data....Pages 21-58 Fuzzy Modeling with Linguistic Integrity: A Tool for Data Mining....Pages 59-90 Nonlinear Identification Using Fuzzy Models....Pages 91-120 Fuzzy Control....Pages 123-150 Predictive Control Based on Fuzzy Models....Pages 151-193 Robust Nonlinear Predictive Control Using Fuzzy Models....Pages 195-206 Conclusions and Future Perspectives....Pages 207-211 Fuzzy set theory can be used in the modeling of systems.