N-MABAC - Nötrosofik MABAC

Formül adımları

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

1. Build SVN decision matrix

   $${\tilde{x}}_{ij}=(T_{ij},I_{ij},F_{ij})\in [0,1{]}^3,T+I+F\le 3$$
   LaTeX

   \tilde{x}_{ij} = (T_{ij}, I_{ij}, F_{ij}) \in [0,1]^3, \quad T+I+F \le 3

2. Score function

   $$S(\tilde{x})=\frac{1+T-2I-F}{2}$$
   LaTeX

   S(\tilde{x}) = \frac{1+T-2I-F}{2}

3. Normalize weights

   $$\sum _j{w}_j=1$$
   LaTeX

   \sum_j w_j = 1

4. Weighted SVN matrix

   $${\tilde{v}}_{ij}=w_j\otimes {\tilde{x}}_{ij}$$
   LaTeX

   \tilde{v}_{ij} = w_j \otimes \tilde{x}_{ij}

5. Border approximation area

   $${\tilde{g}}_j={\left({\prod }_{i=1}^m{\tilde{v}}_{ij}\right)}^{1/m}$$
   LaTeX

   \tilde{g}_j = \left(\prod_{i=1}^m \tilde{v}_{ij}\right)^{1/m}

6. Distance from BAA

   $$q_{ij}=S({\tilde{v}}_{ij})-S({\tilde{g}}_j)$$
   LaTeX

   q_{ij} = S(\tilde{v}_{ij}) - S(\tilde{g}_j)

7. Aggregate

   $$Q_i=\sum _{j=1}^n{q}_{ij}$$
   LaTeX

   Q_i = \sum_{j=1}^n q_{ij}

8. Rank

   $$\text{Rank by aggregated neutrosophic score (descending)}$$
   LaTeX

   \text{Rank by aggregated neutrosophic score (descending)}

Sezgi

Sonucu okuma: Use N-MABAC when expert assessments involve truth (T), indeterminacy (I), and falsity (F) components.

Sık yapılan hatalar

• T+I+F > 3 (violates SVN constraint)