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I realise that the difference lies in the way the defuzzification happens but I don't fully understand it. I've read some papers comparing the outputs from the two models but I'm still not really sure how they are different.

Luke
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3 Answers3

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These are the primary differences between Mandani FIS and Sugeno FIS:

Mamdani FIS

  • Output membership function is present
  • Crisp result is obtained through defuzzification of rules’ consequent
  • Non-continuous output surface
  • MISO (Multiple Input Single Output) and MIMO (Multiple Input Multiple Output) systems
  • Expressive power and Interpretable rule consequents
  • Less flexibility in system design

Sugeno FIS

  • No output membership function is present
  • No defuzzification: crisp result is obtained using weighted average of the rules’ consequent
  • Continuous output surface
  • Only MISO systems
  • Loss of interpretability
  • More flexibility in system design
Visalini Kumaraswamy
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Mamdani- It entails a substantial computational burden. Sugeno - It is computationally efficient. Mamdani- It is well suited to human input. Sugeno- It its well suited to mathematically analysis.

Chan
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  • Thanks, been watching this for a while and no-one summarized it quite so succinctly. – Luke Dec 09 '15 at 09:09
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Mamdani type fuzzy inference gives an output that is a fuzzy set. Sugeno-type inference gives an output that is either constant or a linear (weighted) mathematical expression.

e.g Mamdani: If A is X1, and B is X2, then C is X3. (X1, X2, X3 are fuzzy sets).

Sugeno: If A is X1 and B is X2 then C = ax1 + bx2 + c (linear expression) (a,b,and c are constants)

zack
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