fsr (version 1.0.0)

fsi_add_cs: Adding the consequent to an FSI model

Description

This function adds the consequent to a fuzzy spatial inference (FSI) model. It consists of a set of membership functions labeled with linguistic values.

Usage

fsi_add_cs(fsi, lvar, lvals, mfs, bounds)

Arguments

fsi

The FSI model instantiated with the fsi_create function.

lvar

A character value that represents a linguistic variable of the consequent.

lvals

A character vector that represents linguistic values of the linguistic variable of the consequent.

mfs

A vector of functions created by the genmf of the FuzzyR package.

bounds

A numeric vector that represents the lower and upper bounds of the consequent domain.

Value

An FSI model populated with a consequent.

Details

Each linguistic value defined at the lvals parameter has a membership function defined at the mfs parameter. lvals is a character vector containing the names of linguistic values and mfs is vector containing its corresponding membership functions. Thus, the vectors defined for these two parameters must have the same length. For instance, the first value of lvals is the linguistic value for the first membership function in mfs. In bounds, the lower and upper values correspond to the first and second parameter, respectively.

Examples

# NOT RUN {
library(FuzzyR)

# Create the fsi_model:
fsi <- fsi_create("To visit or not to visit, that is the question", 
                   default_conseq = genmf("trimf", c(10, 30, 60)))

# Create the vector with the linguistic values of the linguistic variable "visiting experience":
lvals_visiting_exp <- c("awful", "average", "great")

# Define the membership function for each linguistic value:
awful_mf <- genmf("trimf", c(0, 0, 20))
average_mf <- genmf("trimf", c(10, 30, 60))
great_mf <- genmf("trapmf", c(40, 80, 100, 100))

# Add the consequent to the FSI model:
fsi <- fsi_add_cs(fsi, "visiting experience", lvals_visiting_exp,
                  c(awful_mf, average_mf, great_mf), c(0, 100))

# }