frbs (version 1.0-0)

frbs.gen: The frbs model generator

Description

The purpose of this function is to generate the frbs model from user-given input. It can be used if rules have already been obtained manually, without employing the learning process, e.g. from expert knowledge.

Usage

frbs.gen(range.input, range.output, num.fvalinput,
    names.varinput, num.fvaloutput, varout.mf,
    names.varoutput, rule, varinp.mf, type.model = 1,
    type.defuz = 1, type.tnorm = 1, type.snorm = 1,
    func.tsk = NULL, method.type = "WM", name = "Sim-0")

Arguments

range.input

a matrix(2 x n) containing the range of the input data.

range.output

a matrix(2 x n) containing the range of the output data.

num.fvalinput

a matrix with the number of fuzzy terms of each input variable.

For example: num.fvalinput <- matrix(c(3,2), nrow = 1)

means that there are two variables where the first variable has three fuzzy terms and the second one has two

varinp.mf

a matrix for constructing the shapes of the membership functions. See fuzzifier.

names.varinput

a list for giving names to the fuzzy terms. See rulebase.

num.fvaloutput

the number of fuzzy terms of the output variable.

For example: num.fvaloutput <- matrix(3, nrow = 1)

means there are 3 fuzzy terms for the first variable (in this case, there is only one variable).

varout.mf

a matrix for constructing the membership functions of the output variable. The form is the same as for the varinp.mf parameter. Please see fuzzifier.

names.varoutput

a list for giving names of the fuzzy terms. The form is the same as for the names.varinput parameter. Please see rulebase.

rule

a list of fuzzy IF-THEN rules. Please see rulebase.

type.model

the type of the model. Please see defuzzifier.

type.defuz

the type of the defuzzification method. Please see defuzzifier.

type.tnorm

the type of the t-norm method. Please see inference.

type.snorm

the type of the s-norm method. Please see inference.

func.tsk

a matrix of parameters of the function on the consequent part using the Takagi Sugeno Kang model. Please see rulebase.

method.type

the type of the selected method. Please see frbs.learn.

name

a name of the simulation.

Value

• The frbs-object.

Examples

##########
## This example shows how to use frbs without
## learning process if we have already rules.
#########
## define shape of membership functions of input variables
varinp.mf <- matrix(c(2,1,3,2,3,2,3,2,3,0,30,60,0,40,0,40,0,40,20,50,80,
                      30,80,30,80,30, 80,40,70, 100,60,100,60,100,60,100,
                      0,0,100,0,100,0,100,0,100), nrow=5, byrow=TRUE)

## define number of fuzzy terms of input variables
num.fvalinput <- matrix(c(3, 2, 2, 2), nrow=1)

## give the names of the fuzzy terms of each input variable
## Note: the names of the fuzzy terms must be unique.
varinput.1 <- c("a1", "a2", "a3")
varinput.2 <- c("b1", "b2")
varinput.3 <- c("c1", "c2")
varinput.4 <- c("d1", "d2")
names.varinput <- c(varinput.1, varinput.2, varinput.3, varinput.4)
range.input <- matrix(c(0,100, 0, 100, 0, 100, 0, 100), nrow=2)
range.output <- matrix(c(0,100), nrow=2)

## define number of fuzzy terms of output variable
num.fvaloutput <- matrix(c(3), nrow=1)

## give the names of the fuzzy terms of the output variable
## Note: the names of the fuzzy terms must be unique.
varoutput.1 <- c("e1", "e2", "e3")
names.varoutput <- c(varoutput.1)

## define the shapes of the membership functions of the input variables
varout.mf <- matrix(c(2,1,3,0,30,60,20,50,80,40,70,100,0,0,100),
                      nrow=5, byrow=TRUE)

type.model <- 1
type.defuz <- 1
type.tnorm <- 1
type.snorm <- 1
method.type <- "WM"
name <- "Sim-0"
## define fuzzy IF-THEN rules,
## Please make sure that each rule has "->" sign.
## If we use the TSK model, we need to define the
## fuzzy terms of the consequent part.
rule <- matrix(c("a1","and","b1","and","c1","and","d1","->","e1",
                 "a2","or","b2","and","c2","and","d2", "->", "e2",
                 "a3","or","b2","and","c2","and","d1", "->", "e3"),
                 nrow=3, byrow=TRUE)

## define function of TSK if we use it or
## set NULL if we use the Mamdani model
func.tsk<-matrix(c(1, 1, 5, 2, 1, 3, 1, 2, 2), nrow=3, ncol=3, byrow=TRUE)

## generate model by frbs.gen that will be used as input for predict
object <- frbs.gen(range.input, range.output, num.fvalinput, names.varinput,
                num.fvaloutput, varout.mf, names.varoutput, rule, varinp.mf,
                type.model, type.defuz, type.tnorm, type.snorm, func.tsk,
                method.type, name)


## testing data
newdata <- matrix(c(10, 10, 10, 10, 10, 20, 12, 23, 30, 30, 23, 23), nrow =3)

## make the predictions
res <- predict(object, newdata)