Structure Of Fuzzy Inference System:

Fuzzy inference is the actual process of mapping from a given input to an output using fuzzy logic. Fuzzy logic starts with the concept of a fuzzy set. Fuzzy inference systems (FIS) are one of the most famous applications of fuzzy logic and fuzzy sets theory

1. Fuzzifier:
It is a transformation of input variables to linguistic ones. Transformation is realized by introduction of so called membership functions, which define both a range of value and a degree of membership.

2. Inference Engine:
An inference mechanism (also called an “inference engine” or “fuzzy inference” module), which emulates the expert’s decision making in interpreting and applying knowledge. There are three main fuzzy logic inference systems:

1. Mamdani type is used in this work that it has fuzzy outputs.
2. Sugeno type.
3. Tsukamoto type.


3. Defuzzifier:
It is converting process Fuzzy output to crisp value and represents converting decisions to actions. Defuzzification operates on the implied fuzzy sets produced by the inference mechanism and combine their effects to provide the "most certain "controller output.