Pythagorean fuzzy set PFS

Gain insights into the definition of pythagorean fuzzy set PFS and standard operations of pythagorean fuzzy set. This guide explains the PFS membership degree, PFS non-membership degree, and includes a pythagorean fuzzy set with example to show its expanded search space

What is a Pythagorean Fuzzy Set (PFS)?

Introduced by Ronald R. Yager in 2013, Pythagorean Fuzzy Sets are a powerful extension of Intuitionistic Fuzzy Sets (IFS). They are designed to handle situations where the sum of membership and non-membership degrees exceeds 1, but the sum of their squares does not.

Mathematical Definition

A Pythagorean Fuzzy Set P in a universe U is defined by a membership function μP(x) and a non-membership function νP(x).

The Pythagorean Condition:
Unlike IFS, where μ + ν ≤ 1, in PFS the rule is:
0 ≤ (μP(x))² + (νP(x))² ≤ 1
Example: Suppose an expert assigns a membership degree of 0.8 and a non-membership degree of 0.5.
In standard IFS: 0.8 + 0.5 = 1.3 (Invalid).
In PFS: (0.8)² + (0.5)² = 0.64 + 0.25 = 0.89 (Valid).

Operations of Pythagorean Fuzzy Sets

Let A and B be two Pythagorean Fuzzy Sets. The fundamental operations are:

  • Union (A ∪ B): Takes the maximum of membership and minimum of non-membership.
    max(μA, μB), min(νA, νB)
  • Intersection (A ∩ B): Takes the minimum of membership and maximum of non-membership.
    min(μA, μB), max(νA, νB)
  • Complement (Ac): Swaps the membership and non-membership values.
    Ac = (νA, μA)

The Degree of Indeterminacy (π)

In PFS, the hesitancy or indeterminacy degree is calculated using the square root:

πP(x) = √(1 – (μP(x)² + νP(x)²))

Pythagorean Fuzzy Numbers (PFN)

A Pythagorean Fuzzy Number is typically represented as a pair p = (μ, ν). These numbers are used extensively in multi-criteria decision-making (MCDM) models.

Arithmetic Operations

For two PFNs, p1 = (μ1, ν1) and p2 = (μ2, ν2):

OperationFormula
Addition (⊕)( √(μ₁² + μ₂² – μ₁²μ₂²), ν₁ν₂ )
Multiplication (⊗)( μ₁μ₂, √(ν₁² + ν₂² – ν₁²ν₂²) )
Solved Example (Addition):
Let p1 = (0.6, 0.4) and p2 = (0.5, 0.3)
Calculation: μ = √(0.6² + 0.5² – 0.6²*0.5²) = √(0.36 + 0.25 – 0.09) = √0.52 ≈ 0.72
Calculation: ν = 0.4 * 0.3 = 0.12
Result: (0.72, 0.12)
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Designed by: Dr. M.U. Mirza

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Machine Learning Fuzzy Sets Computational Math Graph Theory
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