Title :
Fuzzy neural modeling via clustering and support vector machines
Author :
Tovar, Julio César ; Yu, Wen
Author_Institution :
Dept. de Control Automatico, CINVESTAV-IPN, Mexico City
Abstract :
This paper describes a novel fuzzy rule-based modeling approach for some industrial processes. Structure identification is realized by clustering and support vector machines. When the process is slow, fuzzy rules can be obtained automatically. Parameters identification uses the techniques of fuzzy neural networks. A time-varying learning rate assures stability of the modeling error.
Keywords :
fuzzy control; fuzzy neural nets; fuzzy reasoning; learning (artificial intelligence); learning systems; neurocontrollers; nonlinear control systems; parameter estimation; process control; stability; statistical analysis; support vector machines; time-varying systems; clustering method; fuzzy neural modeling; fuzzy neural network techniques; fuzzy rule-based modeling approach; industrial process; modeling error stability; nonlinear system modeling; parameter identification; structure identification; support vector machines; time-varying learning rate; Data mining; Fuzzy control; Fuzzy neural networks; Fuzzy systems; Neural networks; Nonlinear systems; Parameter estimation; Quadratic programming; Support vector machine classification; Support vector machines;
Conference_Titel :
Control Applications, 2007. CCA 2007. IEEE International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-0442-1
Electronic_ISBN :
978-1-4244-0443-8
DOI :
10.1109/CCA.2007.4389200
