This function is one of the main internal functions of the package. It determines the values within the prediction phase.

`frbs.eng(object, newdata)`

object

the `frbs-object`

.

newdata

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:

`rule`

the fuzzy IF-THEN rules

`varinp.mf`

a matrix to generate the shapes of the membership functions for the input variables

`MF`

a matrix of the degrees of the membership functions

`miu.rule`

a matrix of the degrees of the rules

`func.tsk`

a matrix of the Takagi Sugeno Kang model for the consequent part of the fuzzy IF-THEN rules

`predicted.val`

a 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`

.