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Difference between Fuzzification and Defuzzification
Fuzzification is the process of converting a crisp quantity into a fuzzy quantity. On the other hand, defuzzification is the process of translating a fuzzy quantity into a crisp quantity. Read this article to learn more about fuzzification and defuzzification and how they are different from each other.
What is Fuzzification?
Fuzzification may be defined as the process of transforming a crisp set to a fuzzy set or a fuzzy set to fuzzier set. Basically, this operation translates accurate crisp input values into linguistic variables. In a number of engineering applications, it is necessary to defuzzify the result or rather "fuzzy result" so that it must be converted to crisp result.
Fuzzification is done by recognizing various assumed crisp quantities as the non-deterministic and completely uncertain in nature. This uncertainty may be emerged because of imprecision and uncertain that lead variables to be presented by a membership function because they can be fuzzy in nature.
Fuzzification translates the crisp input data into linguistic variables which are represented by fuzzy sets. After that, it applies the membership functions to measure and determine the degree of membership.
What is Defuzzification?
Defuzzification may be defined as the process of reducing a fuzzy set into a crisp set or to convert a fuzzy member into a crisp member. Mathematically, the process of Defuzzification is also called "rounding it off". Defuzzification basically transforms an imprecise data into precise data. However, it is a relatively complex to implement defuzzification as compared to fuzzification.
Defuzzification is basically the reverse process of fuzzification because it converts the fuzzy data into crisp data. In some practical implementations, the defuzzification process is required for crisp control actions to operate the control.
Now, let us discuss the differences between fuzzification and defuzzification.
Difference between Fuzzification and Defuzzification
The following are the important difference between Fuzzification and Defuzzification −
Key |
Fuzzification |
Defuzzification |
|---|---|---|
Definition |
Fuzzification is the process of transforming a crisp set to a fuzzy set or a fuzzy set to fuzzier set. |
Defuzzification is the process of reducing a fuzzy set into a crisp set or converting a fuzzy member into a crisp member. |
Purpose |
Fuzzification converts a precise data into imprecise data. |
Defuzzification converts an imprecise data into precise data. |
Example |
Voltmeter. |
Stepper motor, D/A converter. |
Methods used |
Inference, Rank ordering, Angular fuzzy sets, Neural network. |
Maximum membership principle, Centroid method, Weighted average method, Center of sums. |
Complexity |
Fuzzification is easy. |
Defuzzification is quite complex to implement. |
Approach |
Fuzzification uses if-then rules to fuzzify the crisp value. |
Defuzzification uses center of gravity methods to get centroid of sets. |
Conclusion
The most significant difference that you should note here is that fuzzification converts a precise data into imprecise data, while defuzzification converts an imprecise data into precise data.
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