Transformation of a Mamdani FIS to First Order Sugeno FIS

Javad J. Jassbi; S. H. Alavi; Paulo J.A. Serra; Maria Rita Sarmento de Almeida Ribeiro

doi:10.1109/FUZZY.2007.4295331

Transformation of a Mamdani FIS to First Order Sugeno FIS

Javad J. Jassbi, S. H. Alavi, Paulo J.A. Serra, Maria Rita Sarmento de Almeida Ribeiro

UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

22 Citations (Scopus)

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.

Original language	English
Title of host publication	IEEE International Conference on Fuzzy Systems
Place of Publication	London, United Kingdom
Pages	1-6
Number of pages	6
DOIs	https://doi.org/10.1109/FUZZY.2007.4295331
Publication status	Published - Jul 2007
Event	IEEE International Conference on Fuzzy Systems - Duration: 1 Jan 2007 → …

Conference

Conference	IEEE International Conference on Fuzzy Systems
Period	1/01/07 → …

Keywords

Least squares approximations
Optimization
Adaptive algorithms
Curve fitting
Decision support systems
Diesel engines
Fuzzy inference
Fuzzy logic
Genetic algorithms
Inference engines

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10.1109/FUZZY.2007.4295331

http://www.uninova.pt/ca3/en/docs/07C-FUZZIEEE.pdf

Cite this

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title = "Transformation of a Mamdani FIS to First Order Sugeno FIS",

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 = "Least squares approximations, Optimization, Adaptive algorithms, Curve fitting, Decision support systems, Diesel engines, Fuzzy inference, Fuzzy logic, Genetic algorithms, Inference engines",

author = "Jassbi, {Javad J.} and Alavi, {S. H.} and Serra, {Paulo J.A.} and Ribeiro, {Maria Rita Sarmento de Almeida}",

year = "2007",

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doi = "10.1109/FUZZY.2007.4295331",

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N2 - 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.

AB - 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.

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KW - Optimization

KW - Adaptive algorithms

KW - Curve fitting

KW - Decision support systems

KW - Diesel engines

KW - Fuzzy inference

KW - Fuzzy logic

KW - Genetic algorithms

KW - Inference engines

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Transformation of a Mamdani FIS to First Order Sugeno FIS

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Keywords

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