leebiao.m

D-S证据理论

% 一种基于证据补偿证据冲突的改进方法 李彪
clear;clc
m1a = 0.6; m1b = 0.4; m1c = 0.00;   %原始证据
m2a = 0.25; m2b = 0.4; m2c = 0.35;
m3a = 0.90; m3b = 0.00; m3c = 0.10;
m4a = 0.80; m4b = 0.10; m4c = 0.10;


% input = [y;0.8];
% y = fuzzyanalysis(input);

% mjiao = fuzzyanalysis([m1a;m2a]) + fuzzyanalysis([m1b;m2b]) + fuzzyanalysis([m1c;m2c]); 
% 
% m12a =  fuzzyanalysis([m1a;m2a])/mjiao;  %各个证据的模糊合成
% m12b =  fuzzyanalysis([m1b;m2b])/mjiao;
% m12c =  fuzzyanalysis([m1c;m2c])/mjiao; 

mjiao = m1a*m2a + m1b*m2b + m1c*m2c;
mbing =  m1a*(m2b+m2c) + m1b*(m2a+m2c) + m1c*(m2a+m2b); %冲突大小


%把焦元的乘积改成推理

k= 0; %焦元个数

while mbing>0.67;
% while k<100;
    k = k+1;
    BetP = mbing;
    m = abs(m1a -m2a) + abs(m1b -m2b) +abs(m1c -m2c);   %用焦元的距离度量冲突大小

    newm1a =m1a - BetP*(m1a-m2a)/m/2;    %证据补偿，但保证同一焦元的和不变。
    newm1b =m1b - BetP*(m1b-m2b)/m/2;
    newm1c =m1c - BetP*(m1c-m2c)/m/2;
    %
    newm2a =m2a + BetP*(m1a-m2a)/m/2;
    newm2b =m2b + BetP*(m1b-m2b)/m/2;
    newm2c =m2c + BetP*(m1c-m2c)/m/2;
    %
    m1a = newm1a /(newm1a+newm1b+newm1c);
    m1b = newm1b /(newm1a+newm1b+newm1c);
    m1c = newm1c /(newm1a+newm1b+newm1c);

    %
    m2a = newm2a /(newm2a+newm2b+newm2c);
    m2b = newm2b /(newm2a+newm2b+newm2c);
    m2c = newm2c /(newm2a+newm2b+newm2c);

    %
    mjiao = m1a*m2a + m1b*m2b + m1c*m2c;  %交集
    mbing = m1a*(m2b+m2c) + m1b*(m2a+m2c) + m1c*(m2a+m2b);   %冲突
end

m12a = m1a*m2a/mjiao;
m12b = m1b*m2b/mjiao;
m12c = m1c*m2c/mjiao;


%如果有多个证据，则可以两两进行补偿，保证冲突因子在一定范围内。