show about fuzzy logic

show about fuzzy logic
include
fuzzy inference system
represent fuzzy logic using matlab
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A Fuzzy Inference System (FIS) is a way of mapping an input space to an output space using fuzzy logic
FIS uses a collection of fuzzy membership functions and rules, instead of Boolean logic, to reason about data.
The rules in FIS (sometimes may be called as fuzzy expert system) are fuzzy production rules of the form:
if p then q, where p and q are fuzzy statements.
For example, in a fuzzy rule
if x is low and y is high then z is medium.
Here x is low; y is high; z is medium are fuzzy statements; x and y are input variables; z is an output variable, low, high, and medium are fuzzy sets.

fuzzification:

In the process of fuzzification, membership functions defined on input variables are applied to their actual values so that the degree of truth for each rule premise can be determined.
Fuzzy statements in the antecedent are resolved to a degree of membership between 0 and 1.
If there is only one part to the antecedent, then this is the degree of support for the rule.
If there are multiple parts to the antecedent, apply fuzzy logic operators and resolve the antecedent to a single number between 0 and 1.
Antecedent may be joined by OR; AND operators.
For OR -- max
For AND -- min