PF-WPM - WPM yönteminin Pythagorean uzantısı

Formül adımları

Analiz motorunun yöntem bildirimindeki (manifest F.steps) adımlar; raporlardaki formüllerle aynı kaynaktır.

1. Cost complement; PFWG aggregate.

   $$j\in {\Omega }_c:{\widehat{\alpha }}_{ij}=({\nu }_{ij},{\mu }_{ij});{\mathrm{P}\mathrm{F}\mathrm{W}\mathrm{G}}_w({\alpha }_1,\dots ,{\alpha }_n)=(\prod _j{\mu }_j^{w_j},(1-\prod _j(1-{\nu }_j^2{)}^{w_j}{)}^{1/2});{\tilde{P}}_i={\mathrm{P}\mathrm{F}\mathrm{W}\mathrm{G}}_w({\widehat{\alpha }}_{i1},\dots ,{\widehat{\alpha }}_{in})$$
   LaTeX

   j\in\Omega_c:\;\hat{\alpha}_{ij}=(\nu_{ij},\mu_{ij});\;\mathrm{PFWG}_w(\alpha_1,\ldots,\alpha_n)=\bigl(\prod_j\mu_j^{w_j},\;(1-\prod_j(1-\nu_j^2)^{w_j})^{1/2}\bigr);\;\tilde{P}_i=\mathrm{PFWG}_w(\hat{\alpha}_{i1},\ldots,\hat{\alpha}_{in})

2. Score P_i; rank descending.

   $$P_i=({\tilde{\mu }}_i^P{)}^2-({\tilde{\nu }}_i^P{)}^2;\text{rank descending}$$
   LaTeX

   P_i=(\tilde{\mu}_i^P)^2-(\tilde{\nu}_i^P)^2;\quad\text{rank descending}

Sezgi

Sonucu okuma: pf-wpm extends WPM to handle Pythagorean uncertainty. All arithmetic operations (normalisation, weighting, distance computation) are performed using Pythagorean Fuzzy Number (PFN: μ, ν; μ²+ν² ≤ 1) algebra. The final scores are defuzzified via score function S = μ² − ν² before ranking.

Varsayımlar

• Decision matrix entries are valid Pythagorean Fuzzy numbers/tuples
• Underlying crisp method's compensation assumption holds in uncertain space
• All decision-maker(s) and experts use the same linguistic/uncertainty scale

Ne zaman kullanılmaz

• Classical data sufficient - use base WPM directly (avoid unnecessary uncertainty layer)
• Aggregation operator (PFWA/PFOWA/etc.) not specified - output ambiguous

Sık yapılan hatalar

• Değer-uzayı ihlali: hesaplamadan önce tüm girişlerin PFN: μ ∈ [0,1], ν ∈ [0,1], μ²+ν² ≤ 1 koşulunu sağladığından emin olun.
• Defuzzifikasyon yöntemi sıralamayı etkiler: score function S = μ² − ν² kanonik seçimdir.