code1_temp.rar_som神经网络

function [weight]=SOM_neighb2(weight,train_data_active,j_min,delta,alpha) %% 权值调整幅度分布 % -0.2 % 0.2 % 0.6 % -0.2 0.2 0.6 1 0.6 0.2 -0.2 % 0.6 % 0.2 % -0.2 % 单位距离转化比例为0.4 %% 坐标转换 [x,y]=line_to_array(j_min); % 将1*70向量中的坐标转化为7*10矩阵中的坐标 % 1 8 ··· % 7 14 ··· %% 权值调整过程 %结点靠上边情况 if (x<=2) for m=1:1:x+2 if (y<=2) %结点靠左边 for n=1:1:y+2 distant=sqrt((x-m)^2+(y-n)^2); weight(:,(n-1)*7+m)=weight(:,(n-1)*7+m)-alpha*exp(-distant^2/delta^2)*(weight(:,(n-1)*7+m)-train_data_active); end elseif (y>=9) %结点靠右边 for n=y-2:1:10 distant=sqrt((x-m)^2+(y-n)^2); weight(:,(n-1)*7+m)=weight(:,(n-1)*7+m)-alpha*exp(-distant^2/delta^2)*(weight(:,(n-1)*7+m)-train_data_active); end else for n=y-2:1:y+2 distant=sqrt((x-m)^2+(y-n)^2); weight(:,(n-1)*7+m)=weight(:,(n-1)*7+m)-alpha*exp(-distant^2/delta^2)*(weight(:,(n-1)*7+m)-train_data_active); end end end %结点靠下边情况 elseif (x>=6) for m=x-2:1:7 if (y<=2) %结点靠左边 for n=1:1:y+2 distant=sqrt((x-m)^2+(y-n)^2); weight(:,(n-1)*7+m)=weight(:,(n-1)*7+m)-alpha*exp(-distant^2/delta^2)*(weight(:,(n-1)*7+m)-train_data_active); end elseif (y>=9) %结点靠右边 for n=y-2:1:10 distant=sqrt((x-m)^2+(y-n)^2); weight(:,(n-1)*7+m)=weight(:,(n-1)*7+m)-alpha*exp(-distant^2/delta^2)*(weight(:,(n-1)*7+m)-train_data_active); end else for n=y-2:1:y+2 distant=sqrt((x-m)^2+(y-n)^2); weight(:,(n-1)*7+m)=weight(:,(n-1)*7+m)-alpha*exp(-distant^2/delta^2)*(weight(:,(n-1)*7+m)-train_data_active); end end end %结点正好在中间 else for m=x-2:1:x+2 if (y<=2) %结点靠左边 for n=1:1:y+2 distant=sqrt((x-m)^2+(y-n)^2); weight(:,(n-1)*7+m)=weight(:,(n-1)*7+m)-alpha*exp(-distant^2/delta^2)*(weight(:,(n-1)*7+m)-train_data_active); end elseif (y>=9) %结点靠右边 for n=y-2:1:10 distant=sqrt((x-m)^2+(y-n)^2); weight(:,(n-1)*7+m)=weight(:,(n-1)*7+m)-alpha*exp(-distant^2/delta^2)*(weight(:,(n-1)*7+m)-train_data_active); end else for n=y-2:1:y+2 distant=sqrt((x-m)^2+(y-n)^2); weight(:,(n-1)*7+m)=weight(:,(n-1)*7+m)-alpha*exp(-distant^2/delta^2)*(weight(:,(n-1)*7+m)-train_data_active); end end end end end