模糊化：
它是将清晰数量转换为模糊数量的方法。这可以通过将各种已知的清晰和确定性数量识别为完全不确定且本质上非常不确定来实现。这种不确定性可能是由于模糊性和不精确性而出现的，然后导致变量由隶属函数表示，因为它们本质上是模糊的。
例如，当我说温度为 45°C 时，观看者会将清晰的输入值转换为语言变量，例如对人体有利的温度，热或冷。
去模糊化：
它是模糊化的反演，进行映射是为了将清晰的结果转换为模糊结果，而这里进行映射是为了将模糊结果转换为清晰的结果。
这个过程能够产生一个非模糊控制动作，它说明了推断的模糊控制动作的可能性分布。
去模糊化过程也可以看作是舍入过程，其中模糊集具有一组单位区间上的隶属度值减少到单个标量。
模糊化和去模糊化的区别：
| S.No. | Comparison | Fuzzification | 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. |