frbs (version 3.2-0)

# frbs.eng: 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.

## 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

## 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.

fuzzifier, rulebase, inference and defuzzifier.