Constrained optimization of FIS: interpretability and accuracy

Author

Glorennec, Pierre-Yves

Author_Institution

IRISA, Rennes, France

fYear

2004

fDate

19-23 April 2004

Firstpage

371

Lastpage

372

Abstract

In fuzzy learning, interpretability and accuracy are often antagonistic. In many cases, this dilemma is usually overcome by the changeover from fuzzy inference systems to radial basis neural networks: the system performs well but the interpretability of fuzzy rules is lost. It is not a fatality: constrained optimization methods can both preserve interpretability and increase the accuracy of the fuzzy model.

Keywords

fuzzy systems; inference mechanisms; learning (artificial intelligence); optimisation; radial basis function networks; antagonistic; constrained optimization method; fuzzy inference systems; fuzzy learning; interpretability; radial basis neural network; Constraint optimization; Control systems; Data mining; Fuzzy control; Fuzzy logic; Fuzzy systems; Mathematical model; Neural networks; Optimization methods; Takagi-Sugeno model;

fLanguage

English

Publisher

ieee

Conference_Titel

Information and Communication Technologies: From Theory to Applications, 2004. Proceedings. 2004 International Conference on

Print_ISBN

0-7803-8482-2

Type

conf

DOI

10.1109/ICTTA.2004.1307785

Filename

1307785

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3100364