Title :
Compact TS-fuzzy models through clustering and OLS plus FIS model reduction
Author :
Abonyi, J. ; Roubos, J.A. ; Oosterom, M. ; Szeifert, F.
Author_Institution :
Dept. of Process Eng., Univ. of Veszprem, Hungary
fDate :
6/23/1905 12:00:00 AM
Abstract :
Identification of uncertain and nonlinear systems is an important and challenging problem. Fuzzy models of the Takagi-Sugeno (TS) type may be a good choice to describe such systems; however, in many cases these become soon complex. We propose a three-step method to obtain compact TS-models that can be effectively used to represent complex systems: 1) a new fuzzy clustering method is proposed for identification of compact TS-models; 2) the most relevant consequent variables of the TS-model are selected by an orthogonal least squares (OLS) method based on the obtained clusters; and 3) for selection of relevant antecedent variables, a new method is proposed based on Fisher´s interclass separability (FIS) criterion. The overall approach is demonstrated by means of the MPG (miles per gallon) nonlinear regression benchmark. Results are compared with those obtained by standard linear, neuro-fuzzy and advanced fuzzy clustering-based identification tools
Keywords :
fuzzy set theory; identification; least squares approximations; nonlinear systems; reduced order systems; uncertain systems; Fischer interclass separability; Gath Geva fuzzy clustering; Takagi-Sugeno model; antecedent variables; consequent variables; identification; model reduction; nonlinear systems; orthogonal least squares; uncertain systems; Clustering methods; Control engineering; Function approximation; Fuzzy systems; Information technology; Least squares approximation; Least squares methods; Linear systems; Nonlinear systems; Reduced order systems;
Conference_Titel :
Fuzzy Systems, 2001. The 10th IEEE International Conference on
Conference_Location :
Melbourne, Vic.
Print_ISBN :
0-7803-7293-X
DOI :
10.1109/FUZZ.2001.1008925
