It is the method of transforming a crisp quantity into a fuzzy quantity. This can be achieved by identifying the various known crisp and deterministic quantities as completely nondeterministic and quite uncertain in nature. This uncertainty may have emerged because of vagueness and imprecision which then lead the variables to be represented by a membership function as they cab be fuzzy in nature.
For example, when I say the temperature is 45° Celsius the viewer converts the crisp input value into a linguistic variable like favourable temperature for the human body, hot or cold.
It is the inversion of fuzzification, there the mapping is done to convert the crisp results into fuzzy results but here the mapping is done to convert the fuzzy results into crisp results.
This process is capable of generating a nonfuzzy control action which illustrates the possibility distribution of an inferred fuzzy control action.
Defuzzification process can also be treated as the rounding off process, where fuzzy set having a group of membership values on the unit interval reduced to a single scalar quantity.
Difference between Fuzzification and Defuzzification:
|1.||Basic||Precise data is converted into imprecise data.||Imprecise data is converted into precise data.|
|2.||Definition||Fuzzification is the method of converting a crisp quantity into a fuzzy quantity.||Defuzzification is the inverse process of fuzzification where the mapping is done to convert the fuzzy results into crisp results.|
|3.||Example||Like, Voltmeter||Like, Stepper motor and D/A converter|
|4.||Methods||Intuition, inference, rank ordering, angular fuzzy sets, neural network, etcetera.||Maximum membership principle, centroid method, weighted average method, center of sums, etcetera.|
|5.||Complexity||It is quite simple.||It is quite complicated.|
|6.||Use||It can use IF-THEN rules for fuzzifying the crisp value.||It uses the center of gravity methods to find the centroid of the sets.|