Transformation of a Mamdani FIS to First Order Sugeno FIS

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

19 Citations (Scopus)


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 languageEnglish
Title of host publicationIEEE International Conference on Fuzzy Systems
Place of PublicationLondon, United Kingdom
Number of pages6
Publication statusPublished - Jul 2007
EventIEEE International Conference on Fuzzy Systems -
Duration: 1 Jan 2007 → …


ConferenceIEEE International Conference on Fuzzy Systems
Period1/01/07 → …


  • 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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