Transformation of a Mamdani FIS to First Order Sugeno FIS

Author

Jassbi, Javad ; Alavi, S.H. ; Serra, Paulo J A ; Ribeiro, Rita A.

Author_Institution

Azad Univ. Sci. & Res. Campus, Tehran

fYear

2007

fDate

23-26 July 2007

Firstpage

1

Lastpage

6

Abstract

In many decision support applications, it is important to guarantee the expressive power, easy formalization and interpretability of Mamdani-type fuzzy inference systems (FIS), while ensuring the computational efficiency and accuracy of Sugeno-type FIS. Hence, in this paper we present an approach to transform a Mamdani-type FIS into a Sugeno-type FIS. We consider the problem of mapping Mamdani FIS to Sugeno FIS as an optimization problem and by determining the first order Sugeno parameters, the transformation is achieved. To solve this optimization problem we compare three methods: least squares, genetic algorithms and an adaptive neuro-fuzzy inference system. An illustrative example is presented to discuss the approaches.

Keywords

fuzzy neural nets; fuzzy reasoning; genetic algorithms; Mamdani-type fuzzy inference systems; adaptive neuro-fuzzy inference system; decision support applications; first order Sugeno type fuzzy inference systems; genetic algorithms; least squares; optimization problem; Computational efficiency; Fuzzy logic; Fuzzy set theory; Fuzzy systems; Genetic algorithms; Inference algorithms; Knowledge based systems; Least squares methods; Optimization methods; Robustness;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems Conference, 2007. FUZZ-IEEE 2007. IEEE International

Conference_Location

London

ISSN

1098-7584

Print_ISBN

1-4244-1209-9

Electronic_ISBN

1098-7584

Type

conf

DOI

10.1109/FUZZY.2007.4295331

Filename

4295331