Transformation of a Mamdani FIS to First Order Sugeno FIS

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...

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Bibliographic Details
Main Authors: Jassbi, J., Alavi, S.H., Serra, P.J.A., Ribeiro, R.A.
Format: Conference Proceeding
Language:English
Subjects:
Computational efficiency
Fuzzy logic
Fuzzy set theory
Fuzzy systems
Genetic algorithms
Inference algorithms
Knowledge based systems
Least squares methods
Optimization methods
Robustness
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Summary: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.
ISSN:1098-7584
DOI:10.1109/FUZZY.2007.4295331