[AdaptiveVoterOutput,yc,Is,Rs]=AdaptiveVoterIncScoNBit(y1,y2,y3,y4,y5,Rs,Beta,Alfa,a)

function [AdaptiveVoterOutput,yc,Is,Rs]=AdaptiveVoterIncScoNBit(y1,y2,y3,y4,y5,Rs,Beta,Alfa,a)
[n m]=size(y1);
% Calculate yc majority voting output for modules outputs y1,y2,y3
% ,y4,y5 (Algorithm step 1)
% It does Majority Group Vm described in Referrence [12] and
%  in the paper (page:4 Title:Majority Voter Using Distance Metric)
[Vmax,Vind,T,yc]=MajorityVotingNBit(y1,y2,y3,y4,y5,a);

% Calculate incoherence scores Is for all outputs y1,y2,y3,y4,y5
% (Algorithm step 2)
Is(1)=Beta*HammingDist(y1,yc)/m+(1-Beta)*Rs(1);
Is(2)=Beta*HammingDist(y2,yc)/m+(1-Beta)*Rs(2);
Is(3)=Beta*HammingDist(y3,yc)/m+(1-Beta)*Rs(3);
Is(4)=Beta*HammingDist(y4,yc)/m+(1-Beta)*Rs(4);
Is(5)=Beta*HammingDist(y5,yc)/m+(1-Beta)*Rs(5);

% Select minimum incoherence scores Is and and set corresponding yi 
% to adaptive voter output. (Algorithm step 3)
[MinValue minIndex]=min([Is(1) Is(2) Is(3) Is(4) Is(5)]);
minIndex;
switch minIndex
    case 1,
        AdaptiveVoterOutput=y1;
    case 2,
        AdaptiveVoterOutput=y2;
    case 3,
        AdaptiveVoterOutput=y3;
    case 4,
        AdaptiveVoterOutput=y4;
    case 5,
        AdaptiveVoterOutput=y5;
end
        
% Update incoherence history Rs for all redundant module outputs
% y1,y2,y3,y4,y5 (Algorithm step 4)
Rs(1)=Alfa*HammingDist(y1,AdaptiveVoterOutput)/m+(1-Alfa)*Rs(1);
Rs(2)=Alfa*HammingDist(y2,AdaptiveVoterOutput)/m+(1-Alfa)*Rs(2);
Rs(3)=Alfa*HammingDist(y3,AdaptiveVoterOutput)/m+(1-Alfa)*Rs(3);
Rs(4)=Alfa*HammingDist(y4,AdaptiveVoterOutput)/m+(1-Alfa)*Rs(4);
Rs(5)=Alfa*HammingDist(y5,AdaptiveVoterOutput)/m+(1-Alfa)*Rs(5);