defuzzify: Defuzzification Methods for Fuzzy Comprehensive Evaluation
In zhjx19/mathmodels: Comprehensive Mathematical Modeling Algorithms in R

defuzzify

R Documentation

Defuzzification Methods for Fuzzy Comprehensive Evaluation

Description

Implements defuzzification methods for fuzzy evaluation vectors, including weighted average and maximum membership methods.

Usage

defuzzify(mu, scores, method = "weighted_average")

Arguments

`mu`	Numeric vector, membership degrees for evaluation levels, in 0, 1.
`scores`	Numeric vector, scores corresponding to each evaluation level (e.g., c(100, 80, 60, 40) for "Excellent", "Good", "Fair", "Poor").
`method`	Character, defuzzification method: "weighted_average" or "max_membership".

Value

Numeric, defuzzified output value.

Examples

# Example: Defuzzify fuzzy evaluation vectors for three schemes
mu = c(0.318, 0.351, 0.203, 0.128)
scores = c(30, 60, 75, 90)  # Scores for "Poor", "Fair", "Good", "Excellent"
defuzzify(mu, scores, method = "weighted_average")
defuzzify(mu, scores, method = "max_membership")

zhjx19/mathmodels documentation built on June 2, 2025, 12:18 a.m.