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.
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