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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.

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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.

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Thanks, been watching this for a while and no-one summarized it quite so succinctly. – Luke Dec 9 '15 at 9:09

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)

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