interval valued fuzzy set :

Learn the definition of interval valued fuzzy set and the algebraic operations of interval valued fuzzy set. We look at IVFS membership intervals and provide an interval valued fuzzy set with example to show how ranges represent uncertainty.

What is an Interval-Valued Fuzzy Set (IVFS)?

An Interval-Valued Fuzzy Set is a generalization of the standard fuzzy set. Instead of assigning a single precise membership value (like 0.7), it assigns a range or interval (like [0.6, 0.8]).

The Structure: For an element x, the membership is represented as:
MA(x) = [μAL(x), μAU(x)]
μL: Lower Bound (Minimum degree of membership).
μU: Upper Bound (Maximum degree of membership).
Constraint: 0 ≤ μL ≤ μU ≤ 1.

Example Scenario

If you ask a doctor how “High” a patient’s fever is, the doctor might say: “The membership of this temperature in the ‘High Fever’ set is between 0.7 and 0.9.” This is a perfect application of IVFS.

Set Operations on IVFS

Let A = [aL, aU] and B = [bL, bU] be two interval membership values.

1. Union (OR)

The union is calculated by taking the maximum of the lower bounds and the maximum of the upper bounds.

A ∪ B = [ max(aL, bL), max(aU, bU) ]
Step-by-Step Example: A = [0.3, 0.6] and B = [0.4, 0.8]

Calculation:
Lower Bound: max(0.3, 0.4) = 0.4
Upper Bound: max(0.6, 0.8) = 0.8
Result: [0.4, 0.8]

2. Intersection (AND)

The intersection takes the minimum of both bounds.

A ∩ B = [ min(aL, bL), min(aU, bU) ]
Step-by-Step Example: A = [0.3, 0.6] and B = [0.4, 0.8]

Calculation:
Lower Bound: min(0.3, 0.4) = 0.3
Upper Bound: min(0.6, 0.8) = 0.6
Result: [0.3, 0.6]

Interval-Valued Fuzzy Numbers (IVFN) Arithmetic

When working with numerical calculations, IVFNs use standard interval arithmetic or specific generalized formulas.

OperationFormula for A=[aL, aU] and B=[bL, bU]
Addition (⊕)[ aL + bL – aLbL , aU + bU – aUbU ]
Multiplication (⊗)[ aLbL , aUbU ]
Complement[ 1 – aU , 1 – aL ]

Arithmetic Step-by-Step Examples

Let A = [0.2, 0.5] and B = [0.3, 0.6].

1. Addition (⊕): Lower: 0.2 + 0.3 – (0.2 × 0.3) = 0.5 – 0.06 = 0.44
Upper: 0.5 + 0.6 – (0.5 × 0.6) = 1.1 – 0.30 = 0.80
Result: [0.44, 0.80]
2. Complement (Ac): Lower: 1 – 0.5 = 0.5
Upper: 1 – 0.2 = 0.8
Result: [0.5, 0.8]
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Machine Learning Fuzzy Sets Computational Math Graph Theory
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