ordeness of snn.m

协同神经网络的程序

%序参量演化方程


%the number of order parameter
NumberOfOP = NumOfImage;
%the number of step
NumberOfStep = 400;

%prepare attention parameter
for m=1:NumberOfOP
      lmd(m)=1;
%     lmd(m)=1-abs(vmean(m)-Qmean)/max(Qmean,255-Qmean);
end
%lmd(1)=1.2;

maxL = -1000;
minL = 1000;
for k=1:NumberOfOP
   if(lmd(k)>maxL) maxL = lmd(k);end
   if(lmd(k)<minL) minL = lmd(k);end
end
   
%parameter
B=1;C=1;

for n=1:NumberOfStep
    %global field 
    D(n)=0;
    for m=1:NumberOfOP
      D(n)=D(n)+(B+C)*A(n,m)*A(n,m);
    end
    
    %entropy
    if 0
	    sumA = sum(A(n,k));
	    H(n)=0;
	    for k=1:NumberOfOP
	       A(n,k)=A(n,k)/sumA;
   	    H(n)=H(n)-A(n,k)log(A(n,k));
       end
    end
    
    %specialzation
    if 0
       
    end
    
      
    %evloution function
    speed = 0.1/D(n);
    %speed = 0.3;
    
    for k=1:NumberOfOP
      A(n+1,k)=speed * A(n,k)*(lmd(k)-D(n)+B*A(n,k)*A(n,k))+A(n,k);
    end
    
 end
 
%A is the order parameter,draw it;
figure
plot(A)
axis([1,NumberOfStep,-0.5,max(sqrt(minL),minA)]+0.3)
%plot(D)