"Source code" research on Operational effectiveness evaluation based on extended Bayesian method fusion

% source "Research on operational effectiveness evaluation based on extended Bayesian method Fusion" clearclc% qualitative indicators T2 T7 t8% The following kcap and CCAP data table 6, split table 6 into two matrices. Horizontal axis kcap:% of knowledge% ordinate CCAP: satisfaction%      Kcap and Ccap have the same location combined to correspond to a point%kcap and ccap on the belief map: line correspondence expert (3 experts), column corresponding indicator (3 indicators) kcap=[0.8 0.78 0.85;      0.7 0.8 0.7; 0.65 0.7 0.6];      ccap=[0.85 0.82 0.8;      0.75 0.75 0.78; 0.8 0.8 0.75;]; P=BAYESRH (KCAP,CCAP);% Quantitative indicator T1 T3 T4 T5 T6 t9% The following KCAP1 data corresponding table 8,ccap1 are 0.85% horizontal axis Kcap1: knowledge% ordinate Ccap1: satisfaction%      The same position of KCAP1 and Ccap1 is combined to correspond to a point%kcap1 and Ccap1 on the belief map: line Correspondence Test scheme (3 pilot schemes), column corresponding indicator (6 indicators) kcap1=[0.85 0.67 1.00 0.67 0.67 0.75;      0.80 0.53 0.75 0.53 0.47 0.63; 0.75 0.33 0.60 0.60 0.60 0.50]; Ccap1=0.85*ones (3,6); P1=BAYESRH (KCAP1,CCAP1); w=[0.16 0.11 0.09 0.17 0.08 0.09 0.09 0.11 0.1]; E=[P1 (1) P (1) P1 (2:5) P (2:3) P1 (6)];    Ev=w*e ' function p=bayesrh (kcap,ccap) [M,n]=size (Kcap);%ccap the same size as for i=1:n Fenzi=1;    Temp=1;        For J=1:m fenzi=fenzi* (Kcap (j,i) *ccap (j,i) + (1-kcap (j,i)) * (1-ccap (j,i)));    Temp=temp* ((1-kcap (j,i)) *ccap (j,i) +kcap (j,i) * (1-ccap (j,i))); End P (i) =fenzi/(fenzi+temp); end

"Source code" research on Operational effectiveness evaluation based on extended Bayesian method fusion