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Difference Between Fuzzification and Defuzzification

The process of the fuzzy inferencing system is fuzzification and defuzzification, where fuzzification readers the exact amount as a fuzzy quantity, and defuzzification converts the fuzzy quantity into a crisp one. The methods come in handy during the fuzzy inference process, which uses fuzzy to create a mapping from an input to an output. This mapping serves as the foundation for decision-making and pattern recognition. Let us discuss some more differences between Fuzzification and Defuzzification with the help of the comparison given below.

What is Fuzzification?

The process of turning a crisp amount into a fuzzy quantity is called fuzzification. This is accomplished by acknowledging that the many presumptively crisp and deterministic values are in fact wholly nondeterministic and extremely unpredictable. The variables may have been fuzzy in nature, which caused them to be represented by a membership function, and uncertainty may have arisen as a result. Accurate crisp input values are converted into linguistic variables represented by fuzzy sets by the procedure. The degree of membership is then calculated by applying membership functions to the measures.

What is Defuzzification?

Defuzzification is the opposite of fuzzification, which entails mapping to turn fuzzy results into clear ones. It converts the space of fuzzily specified control actions over a universe of discourse output into a space of sharply defined control actions. Crisp control actions in some practical implementations are required to operate the control, necessitating the requirement for defuzzification. A nonfuzzy control action that displays the possibility distribution of an inferred fuzzy control action is produced by the technique. Defuzzification is a method that reduces a fuzzy containing a collection of membership values until the interval to a single scalar number. It can be compared to the rounding-off procedure. 

Difference between fuzzification and defuzzification | Fuzzification Vs Defuzzification:

  • Fuzzification is precise data converted into precise data. While in defuzzification imprecise data is converted into precise data.
  • The process of turning a crisp amount into a fuzzy quantity is called fuzzification. While in defuzzification is the inverse process of fuzzification where the mapping is done to convert the fuzzy results into crisp results. 
  • An example of fuzzification is a voltmeter. While the example of defuzzification is the stepper motor and D/A converter.
  • Complexity is quite simple in fuzzification. While defuzzification is quite complicated. 
  • The method of fuzzification is intuition, inference, rank, ordering, angular fuzzy sets, neural network, etcetera. While the method of defuzzification maximum membership principle, the centroid method. weighted average method, the center of sums, etcetera. 
  • Fuzzification users can use If-then rules for fuzzifying the crisp value. While defuzzification uses the center of gravity method to find the centroid of the sets. 
  • Fuzzification is easy, but defuzzification is quite complex to implement. 
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