SQL
October 4, 2023
DFD
October 4, 2023
SQL
October 4, 2023
DFD
October 4, 2023

Software-process-models

Question 5
Consider the following models:

M1: Mamdani model

M2: Takagi-Sugeno-Kang model

M3: Kosko’s additive model(SAM)

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 5 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 [1]. 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.

Correct Answer: B
Question 5 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 [1]. 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.

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