Title :
A new identification method for linguistic fuzzy models
Author :
Babuska, R. ; Verbruggen, H.B.
Author_Institution :
Dept. of Electr. Eng., Delft Univ. of Technol., Netherlands
Abstract :
This paper presents a method for deriving a linguistic fuzzy model from an already identified fuzzy linear model. The approach is based on the novel concept of complementary fuzzy partition which is derived from the partition of a fuzzy linear model. It combines a well established identification method for fuzzy linear models with a good semantic interpretation capabilities of linguistic fuzzy models. The method is applied to the identification of a linguistic fuzzy model of a highly nonlinear process. It is shown that along with the semantic meaning the global numerical accuracy is also improved, compared to the original fuzzy linear model
Keywords :
computational linguistics; fuzzy set theory; identification; modelling; complementary fuzzy partition; fuzzy clustering; fuzzy linear model; identification; linguistic fuzzy models; membership functions; semantic interpretation; Fuzzy sets; Fuzzy systems; Laboratories; Linear regression; Mathematical model; Nonlinear systems; Takagi-Sugeno-Kang model;
Conference_Titel :
Fuzzy Systems, 1995. International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium., Proceedings of 1995 IEEE Int
Conference_Location :
Yokohama
Print_ISBN :
0-7803-2461-7
DOI :
10.1109/FUZZY.1995.409790
