fsr (version 2.0.1)

fsi_add_cs: Add the consequent to an FSI model

Description

fsi_add_cs() 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)

Value

An FSI model populated with a consequent.

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 contains linguistic values of the linguistic variable of the consequent.

mfs

A vector of membership functions (see examples below).

bounds

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

Details

The fsi_add_cs() function adds the consequent to an FSI model. Each linguistic value defined in lvals has a corresponding membership function defined in mfs. Thus, these two parameters must have the same length. For instance, the first value of lvals defines the linguistic value of the first membership function in mfs. In bounds, the lower and upper values correspond to the first and second parameter, respectively.

References

Carniel, A. C.; Galdino, F.; Philippsen, J. S.; Schneider, M. Handling Fuzzy Spatial Data in R Using the fsr Package. In Proceedings of the 29th International Conference on Advances in Geographic Information Systems (AM SIGSPATIAL 2021), pp. 526-535, 2021.

Underlying concepts and formal definitions of FSI models are introduced in:

• Carniel, A. C.; Schneider, M. Fuzzy inference on fuzzy spatial objects (FIFUS) for spatial decision support systems. In Proceedings of the 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2017), pp. 1-6, 2017.

Examples

# Defining two different types of membership functions
trap_mf <- function(a, b, c, d) {
  function(x) {
    pmax(pmin((x - a)/(b - a), 1, (d - x)/(d - c), na.rm = TRUE), 0)
  }
}

trim_mf <- function(a, b, c) {
  function(x) {
    pmax(pmin((x - a)/(b - a), (c - x)/(c - b), na.rm = TRUE), 0)
  }
}

# Creating the FSI model
fsi <- fsi_create("To visit or not to visit, that is the question", 
                   default_conseq = trim_mf(10, 30, 60))

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

# Defining the membership function for each linguistic value
awful_mf <- trim_mf(0, 0, 20)
average_mf <- trim_mf(10, 30, 60)
great_mf <- trap_mf(40, 80, 100, 100)

# Adding 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))