Rule-base self-generation and simplification for data-driven fuzzy models

Author

Chen, Min-You ; Linkens, D.A.

Author_Institution

Dept. of Autom. Control & Syst. Eng., Univ. of Sheffield, UK

Volume

1

fYear

2001

fDate

6/23/1905 12:00:00 AM

Firstpage

424

Lastpage

427

Abstract

A rule-base self-extraction and simplification method is proposed to establish interpretable fuzzy models from numerical data. A fuzzy clustering technique is used to extract the initial fuzzy rule-base. The number of fuzzy rules is determined by the proposed fuzzy partition validity index. To reduce the complexity of fuzzy models without decreasing the model accuracy significantly, some approximate similarity measures are presented and a parameter fine-tuning mechanism is introduced to improve the accuracy of the simplified model. The simplified fuzzy model has good balance between accuracy and transparency

Keywords

fuzzy set theory; fuzzy systems; modelling; pattern clustering; approximate similarity measures; data-driven fuzzy models; fuzzy clustering technique; fuzzy partition validity index; interpretable fuzzy models; model accuracy; numerical data; parameter fine-tuning mechanism; rule-base self-generation; rule-base simplification; Automatic control; Clustering algorithms; Data mining; Fuzzy control; Fuzzy sets; Fuzzy systems; Modeling; Partitioning algorithms; Predictive models; Systems engineering and theory;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2001. The 10th IEEE International Conference on

Conference_Location

Melbourne, Vic.

Print_ISBN

0-7803-7293-X

Type

conf

DOI

10.1109/FUZZ.2001.1007339

Filename

1007339