Both Mamdani and Sugeno FIS are universal approximators, i.e., they approximate any continuous functions to any degree of accuracy Mamdani type FIS gives an output that is a fuzzy set, whereas Sugeno-type inference gives an output that is either constant or a linear (weighted) mathematical expression. According to Ying et al. (1998) the minimal system configurations of the Sugeno and Mamdani FIS's are comparable. However, In terms of performance and adaptability to other user defined environment making Sugeno-type FIS highly flexible and optimization of FIS could be done by a well defined set of algorithms such as ANNs or GA.
Hao Ying, Yongsheng Ding, Shaokuan Li' and Shihuang Shao (1998) 'Typical Takagi-Sugeno and Mamdani Fuzzy Systems as Universal Approximators: Necessary Conditions and Comparison, IEEE Fuzzy Systems Proceedings, PP: 824-828.