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

Volume

2

fYear

1995

fDate

20-24 Mar 1995

Firstpage

905

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/FUZZY.1995.409790

Filename

409790