Q1: In fuzzy logic, what does a membership function represent?
- (A) A function that maps each input to a fuzzy set
- (B) A function that maps each input to a crisp value
- (C) A function that maps fuzzy sets to outputs
- (D) A function that computes the degree of truth of an input
Answer: (A) A function that maps each input to a fuzzy set
Q2: In fuzzy logic, which of the following is NOT a commonly used t-norm (triangular norm)?
- (A) Minimum T-norm
- (B) Product T-norm
- (C) Maximum T-norm
- (D) Lukasiewicz T-norm
Answer: (C) Maximum T-norm
Q3: What is the output of a fuzzy inference system?
- (A) A crisp output from a fuzzy set
- (B) A fuzzy output
- (C) A set of rules
- (D) A continuous function
Answer: (B) A fuzzy output
Q4: Which of the following is a key feature of fuzzy logic compared to classical Boolean logic?
- (A) Fuzzy logic allows partial membership values between 0 and 1
- (B) Fuzzy logic uses only true and false values
- (C) Fuzzy logic does not use set theory
- (D) Fuzzy logic operates only with continuous variables
Answer: (A) Fuzzy logic allows partial membership values between 0 and 1
Q5: Which method is commonly used in fuzzy logic to perform the aggregation of multiple fuzzy sets?
- (A) Minimum aggregation
- (B) Maximum aggregation
- (C) Arithmetic mean aggregation
- (D) Weighted average aggregation
Answer: (D) Weighted average aggregation
Q6: What is the primary advantage of using fuzzy logic in control systems?
- (A) It allows for precise control based on exact measurements
- (B) It simplifies complex control systems into binary decisions
- (C) It allows for handling of imprecision and uncertainty in decision-making
- (D) It eliminates the need for human intervention in control systems
Answer: (C) It allows for handling of imprecision and uncertainty in decision-making
Q7: In fuzzy logic, what does defuzzification refer to?
- (A) Converting a fuzzy output to a crisp value
- (B) Mapping input values to fuzzy sets
- (C) Combining multiple fuzzy sets into one
- (D) Defining membership functions for fuzzy sets
Answer: (A) Converting a fuzzy output to a crisp value
Q8: What is the purpose of fuzzy rules in a fuzzy inference system?
- (A) To provide crisp inputs for fuzzy sets
- (B) To convert crisp outputs to fuzzy outputs
- (C) To map fuzzy inputs to fuzzy outputs based on certain conditions
- (D) To optimize the membership function of fuzzy sets
Answer: (C) To map fuzzy inputs to fuzzy outputs based on certain conditions