Similarity measures in fuzzy rule base simplification.
IEEE Trans Syst Man Cybern B Cybern
; 28(3): 376-86, 1998.
Article
em En
| MEDLINE
| ID: mdl-18255954
ABSTRACT
In fuzzy rule-based models acquired from numerical data, redundancy may be present in the form of similar fuzzy sets that represent compatible concepts. This results in an unnecessarily complex and less transparent linguistic description of the system. By using a measure of similarity, a rule base simplification method is proposed that reduces the number of fuzzy sets in the model. Similar fuzzy sets are merged to create a common fuzzy set to replace them in the rule base. If the redundancy in the model is high, merging similar fuzzy sets might result in equal rules that also can be merged, thereby reducing the number of rules as well. The simplified rule base is computationally more efficient and linguistically more tractable. The approach has been successfully applied to fuzzy models of real world systems.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
IEEE Trans Syst Man Cybern B Cybern
Assunto da revista:
ENGENHARIA BIOMEDICA
Ano de publicação:
1998
Tipo de documento:
Article