frbs.eng {frbs}

The prediction phase

Description

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

Usage

frbs.eng(object, newdata)

Arguments

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.

Details

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.

Value

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

See Also

fuzzifier, rulebase, inference and defuzzifier.