📜  模糊化和反模糊化之间的区别

📅  最后修改于: 2021-04-16 08:51:05             🧑  作者: Mango

模糊化:
它是将清脆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的和确定性的量识别为本质上完全不确定的且非常不确定的来实现。这种不确定性可能是由于模糊性和不精确性而出现的,这会导致变量在本质上是模糊的,因此由隶属函数表示。

例如,当我说温度为摄氏45度时,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(冷热)。

模糊化:
这是模糊化的反演,在这里进行了映射以将清晰的结果转换为模糊结果,但是在这里进行了映射以将模糊的结果转换为清晰的结果。
该过程能够产生非模糊控制动作,该非模糊控制动作示出了推断的模糊控制动作的可能性分布。

模糊化过程也可以看作是舍入过程,其中具有在单位间隔上的一组隶属度值的模糊集减少为单个标量的模糊集。

模糊化和反模糊化之间的区别:

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.