Hiirsalmi,M., Kotsakis,E.  Pesonen,A. and Wolski,A.,  
"Discovery of Fuzzy Models from Observation Data" , 
Research Report TTE1-2000-43, VTT Information Technology, 
Espoo, Finland, December 2000.

Abstract

Methods for automatic identification of fuzzy models 
for the purposes of real-time industrial process monitoring 
are studied and tested. Theoretical fuzzy and neurofuzzy approaches 
to identification of Mamdani and Takagi-Sugeno inference models 
are summarized. Support of fuzzy inference in the active database 
system RapidBase is discussed. Commercial products Matlab 
and fuzzyTech are tested in a case study of predicting abnormal 
states in a waste water treatment plant. The most challenging 
part of the modeling turns out to be the structural identification, 
that is the derivation of the rule format and the selected variables. 
Best results are produced, in Matlab, by using neurofuzzy learning 
(ANFIS) together with the sequential forward search and, 
in fuzzyTech, with the learning of degree of rule support. 
Applicability of the studied methods to automatic extraction 
of RapidBase fuzzy monitoring models from measurement data 
is discussed