October 4, 2023
October 4, 2023
October 4, 2023
###### Software-Engineering
October 4, 2023
 Question 8
Consider the following models:

M1: Mamdani model

M2: Takagi-Sugeno-Kang model

Which of the following option contains example of additive rule model?

 A Only M1 and M2 B Only M2 and M3 C Only M1 and M3 D M1, M2 and M3
Question 8 Explanation:
Mamdani Fuzzy Inference Systems
Mamdani fuzzy inference was first introduced as a method to create a control system by synthesizing a set of linguistic control rules obtained from experienced human operators . In a Mamdani system, the output of each rule is a fuzzy set.
Since Mamdani systems have more intuitive and easier to understand rule bases, they are well-suited to expert system applications where the rules are created from human expert knowledge, such as medical diagnostics.
Sugeno Fuzzy Inference Systems
Sugeno fuzzy inference, also referred to as Takagi-Sugeno-Kang fuzzy inference, uses singleton output membership functions that are either constant or a linear function of the input values. The defuzzification process for a Sugeno system is more computationally efficient compared to that of a Mamdani system, since it uses a weighted average or weighted sum of a few data points rather than compute a centroid of a two-dimensional area.
You can convert a Mamdani system into a Sugeno system using the convert To Sugeno function. The resulting Sugeno system has constant output membership functions that correspond to the centroids of the Mamdani output membership functions.  Question 8 Explanation:
Mamdani Fuzzy Inference Systems
Mamdani fuzzy inference was first introduced as a method to create a control system by synthesizing a set of linguistic control rules obtained from experienced human operators . In a Mamdani system, the output of each rule is a fuzzy set.
Since Mamdani systems have more intuitive and easier to understand rule bases, they are well-suited to expert system applications where the rules are created from human expert knowledge, such as medical diagnostics.
Sugeno Fuzzy Inference Systems
Sugeno fuzzy inference, also referred to as Takagi-Sugeno-Kang fuzzy inference, uses singleton output membership functions that are either constant or a linear function of the input values. The defuzzification process for a Sugeno system is more computationally efficient compared to that of a Mamdani system, since it uses a weighted average or weighted sum of a few data points rather than compute a centroid of a two-dimensional area.
You can convert a Mamdani system into a Sugeno system using the convert To Sugeno function. The resulting Sugeno system has constant output membership functions that correspond to the centroids of the Mamdani output membership functions.  