This function is one of the main internal functions of the package. It determines the values within the prediction phase.
frbs.eng(object, newdata)the frbs-object.
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:
rulethe fuzzy IF-THEN rules
varinp.mfa matrix to generate the shapes of the membership functions for the input variables
MFa matrix of the degrees of the membership functions
miu.rulea matrix of the degrees of the rules
func.tska matrix of the Takagi Sugeno Kang model for the consequent part of the fuzzy IF-THEN rules
predicted.vala matrix of the predicted values
This function involves four different processing steps on fuzzy rule-based systems.
Firstly, the rulebase (see rulebase) validates
the consistency of the fuzzy IF-THEN rules form. Then, the fuzzification
(see 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.
fuzzifier, rulebase, inference
and defuzzifier.