The process of fuzzy reasoning
Inference refers to the process of fuzzy reasoning.
inference(MF, rule, names.varinput, type.tnorm, type.snorm)
a matrix of the degrees of membership functions which is a result of the
a matrix or list of fuzzy IF-THEN rules. See
a list of names of the input variables.
a value which represents the type of t-norm to be used:
a value which represents the type of s-norm to be used:
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 IF-THEN 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 IF-THEN 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 (
It provides linguistic hedge (
there are several methods for the t-norm and s-norm.
a matrix of the degrees of the rules.
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