Updated 16 May 2020
In a Fuzzy logic system we make relations between inputs and outputs by creating rules between input and outputs, it can be single or multiple inputs and outputs. A word Fuzzy means something vague or indistinct. Let’s understand it with an example of a washing machine. You put some clothes in the washing machine to wash but you are confused or indistinct how much time is needed to make your clothes completely clean. So there is a fuzzy controller in your washing machine, based on weight of the clothes or type of the clothes.
Why Use Fuzzy Logic?
When You don’t have data or you have to use your reasoning to create data
If you have a proper data set for inputs and outputs then you can go for machine learning and neural network, but if you have to use your logic to create data then go for fuzzy logic. For example, in the case of the washing machine your input is clothes and output is time (till what time washing machine will be on), but there is no such numeric data for clothes, so you have to create data for clothes in such a way that you can train the fuzzy logic. Let’s say to light clothes I assigned value 1 to 3, to medium heavy clothes I assigned value between 3 to 7, and to heavy clothes values lied between 6 to 10, in this case I used my reasoning to create data and this kind of data can be applied in fuzzy logic only. Sometimes we use fuzzy logic to create data for ML and DL.