Mathematics Stack Exchange is a question and answer site for people studying math at any level and professionals in related fields. Join them; it only takes a minute:

Sign up
Here's how it works:
  1. Anybody can ask a question
  2. Anybody can answer
  3. The best answers are voted up and rise to the top

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.

share|cite|improve this question
up vote 1 down vote accepted

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.

share|cite|improve this answer
    
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)

share|cite|improve this answer
    
For some basic information about writing math at this site see e.g. here, here, here and here. – user93957 Dec 28 '13 at 10:36

Your Answer

 
discard

By posting your answer, you agree to the privacy policy and terms of service.

Not the answer you're looking for? Browse other questions tagged or ask your own question.