What is the difference between mamdani and sugeno in fuzzy logic? 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.
 A: 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.
A: 
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

A: 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) 
