Structure determination in fuzzy modeling: a fuzzy CART approach

Author

Jang, Jyh-Shing Roger

Author_Institution

MathWorks Inc., Natick, MA, USA

fYear

1994

fDate

26-29 Jun 1994

Firstpage

480

Abstract

This paper presents an innovative approach to the structure determination problem in fuzzy modeling. By using the well-known CART (classification and regression tree) algorithm as a quick preprocess, the proposed method can roughly estimate the structure (numbers of membership functions and number of fuzzy rules, etc.) of a fuzzy inference system; the parameter identification is then carried out by the hybrid learning scheme developed in our previous work. Moreover, the identified fuzzy inference system has the property that the total of firing strengths is always equal to one; this speeds up learning processes and reduces round-off errors

Keywords

decision theory; fuzzy logic; identification; inference mechanisms; learning (artificial intelligence); statistical analysis; trees (mathematics); adaptive network based fuzzy inference system; classification and regression tree; decision tree; firing strengths; fuzzy CART; fuzzy inference system; fuzzy modeling; fuzzy rules; hybrid learning scheme; membership functions; parameter identification; round-off errors; structure estimation; Classification tree analysis; Fuzzy reasoning; Fuzzy sets; Fuzzy systems; Inference algorithms; Mesh generation; Parameter estimation; Predictive models; Regression tree analysis; Roundoff errors;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the Third IEEE Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1896-X

Type

conf

DOI

10.1109/FUZZY.1994.343738

Filename

343738