The process of fuzzy reasoning
Description
Inference refers to the process of fuzzy reasoning.
Usage
1  inference(MF, rule, names.varinput, type.tnorm, type.snorm)

Arguments
MF 
a matrix of the degrees of membership functions which is a result of the 
rule 
a matrix or list of fuzzy IFTHEN rules. See 
names.varinput 
a list of names of the input variables. 
type.tnorm 
a value which represents the type of tnorm to be used:

type.snorm 
a value which represents the type of snorm to be used:

Details
In this function, fuzzy reasoning is conducted based on Mamdani and Takagi Sugeno Kang model. Furthermore, there are some formula for conjunction and disjunction operators.
The Mamdani model: A fuzzy system with, e.g., two inputs x1 and x2 (antecedents) and a single output y (consequent) is described by the following fuzzy IFTHEN rule:
IF x1 is A1 and x2 is A2 THEN y is B
where A1 and A2 are the fuzzy sets representing the antecent pairs and B is the fuzzy set representing the consequent.
The Takagi Sugeno Kang model: Suppose we have two inputs x1 and x2 and output y, then the fuzzy IFTHEN rule is as follows:
IF x1 is A1 and x2 is A2 THEN y is y = f(x1, x2)
where y = f(x1, x2) is a crisp function in the consequent part which is usually a polynomial function, and A1 and A2 are the fuzzy sets representing the antecent pairs.
Futhermore, this function has the following capabilities:
It supports unary operators (not) and binary operators (
AND
andOR
).It provides linguistic hedge (
extremely
,very
,somewhat
, andslightly
).there are several methods for the tnorm and snorm.
Value
a matrix of the degrees of the rules.
See Also
defuzzifier
, rulebase
, and fuzzifier
.