# frbs.eng: The prediction phase In frbs: Fuzzy Rule-Based Systems for Classification and Regression Tasks

## Description

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

## Usage

 1 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

fuzzifier, rulebase, inference and defuzzifier.

frbs documentation built on May 29, 2017, 9:08 p.m.