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 industrialcontrol.
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