This function is one of the main internal functions of the package. It determines the values within the prediction phase.
a matrix (\(m \times n\)) of data for the prediction process, where \(m\) is the number of instances and \(n\) is the number of input variables.
A list with the following items:
the fuzzy IF-THEN rules
a matrix to generate the shapes of the membership functions for the input variables
a matrix of the degrees of the membership functions
a matrix of the degrees of the rules
a matrix of the Takagi Sugeno Kang model for the consequent part of the fuzzy IF-THEN rules
a matrix of the predicted values
This function involves four different processing steps on fuzzy rule-based systems.
Firstly, the rulebase (see
the consistency of the fuzzy IF-THEN rules form. Then, the fuzzification
fuzzifier) transforms crisp values
into linguistic terms. Next, the inference calculates the degree of rule strengths using
the t-norm and the s-norm.
Finally, the defuzzification process calculates the results of the model using the Mamdani
or the Takagi Sugeno Kang model.