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

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.

frbs documentation built on Dec. 16, 2019, 1:19 a.m.