Title :
A new scheme for fuzzy rule-based system identification and its application to self-tuning fuzzy controllers
Author :
Pal, Kuhu ; Mudi, Rajani K. ; Pal, Nikhil R.
Author_Institution :
Pragati Nagar, Hooghly, India
fDate :
8/1/2002 12:00:00 AM
Abstract :
There are many important issues that need to be resolved for identification of a fuzzy rule-based system using clustering. We address three such important issues: 1) deciding on the proper domain(s) of clustering; 2) deciding on the number of rules; and 3) getting an initial estimate of parameters of the fuzzy systems. We justify that one should start with separate clustering of X (input) and Y (output). We propose a scheme to establish correspondence between the clusters obtained in X and Y. The correspondence dictates whether further splitting/merging of clusters is needed or not. If X and Y do not exhibit strong cluster substructures, then again clustering of X* (input data augmented by the output data) exploiting the results of separate clustering of X and Y, and of the correspondence scheme is recommended. We justify that usual cluster validity indices are not suitable for finding the number of rules, and the proposed scheme does not use any cluster validity index. Three methods are suggested to get the initial estimate of membership functions (MFs). The proposed scheme is used to identify the rule base needed to realize a self-tuning fuzzy PI-type controller and its performance is found to be quite satisfactory
Keywords :
adaptive control; fuzzy control; fuzzy systems; identification; knowledge based systems; parameter estimation; pattern clustering; self-adjusting systems; two-term control; cluster validity index; clustering; fuzzy rule extraction; fuzzy rule-based system identification; membership functions; merging; parameter estimation; performance; self-tuning fuzzy PI-type controller; self-tuning fuzzy controllers; splitting; Clustering algorithms; Clustering methods; Control systems; Data mining; Fuzzy control; Fuzzy systems; Knowledge based systems; Merging; Parameter estimation; System identification;
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
DOI :
10.1109/TSMCB.2002.1018766