%初始化正向權(quán)值w和反向權(quán)值v
w=rands(18,2)/2+0.5;
v=rands(5,18)/2+0.5;
%輸入向量P和目標(biāo)向量T
P=[0 0;0.5 0.5;0 0.5;1 3;0.5 1;1 0.5];
T=[1 0 0 0 0;1 0 0 0 0;0 1 0 0 0;0 0 1 0 0;0 0 0 1 0;0 0 0 0 1];
T_out=T;
%設(shè)定學(xué)習(xí)步數(shù)為1000次
epoch=1000;
%歸一化輸入向量P
for i=1:6
    if P(i,:)==[0 0]
        P(i,:)=P(i,:);
    else
        P(i,:)=P(i,:)/norm(P(i,:));
    end
end
%開(kāi)始訓(xùn)練
while epoch>0
for j=1:6 
%歸一化正向權(quán)值w
    for i=1:18
        w(i,:)=w(i,:)/norm(w(i,:));
        s(i)=P(j,:)*w(i,:)';
    end
    %求輸出為最大的神經(jīng)元，即獲勝神經(jīng)元
    temp=max(s);
    for i=1:18
        if temp==s(i)
            count=i;
        end
    end
%將所有競(jìng)爭(zhēng)層神經(jīng)元的輸出置零
    for i=1:18
        s(i)=0;
    end
%獲勝神經(jīng)元的輸出置1
    s(count)=1;
%權(quán)值調(diào)整
    w(count,:)=w(count,:)+0.1*[P(j,:)-w(count,:)];
    w(count,:)=w(count,:)/norm(w(count,:));
    v(:,count)=v(:,count)+0.1*(T(j,:)'-T_out(j,:)');
%計(jì)算網(wǎng)絡(luò)輸出
    T_out(j,:)=v(:,count)';
end
    %訓(xùn)練次數(shù)遞減
    epoch=epoch-1;
end
%訓(xùn)練結(jié)束
T_out
%網(wǎng)絡(luò)回想
%網(wǎng)絡(luò)的輸入模式Pc
Pc=[0.5 1;1 3];
%初始化Pc
for i=1:2
    if Pc(i,:)==[0 0]
        Pc(i,:)=Pc(i,:);
    else
        Pc(i,:)=Pc(i,:)/norm(Pc(i,:));
    end
end
%網(wǎng)絡(luò)輸出
Outc=[0 0 0 0 0;0 0 0 0 0];
for j=1:2
    for i=1:18
        sc(i)=Pc(j,:)*w(i,:)';
    end
    tempc=max(sc);
    for i=1:18
        if tempc==sc(i)
            countp=i;
        end
        sc(i)=0;
    end
    sc(countp)=1;
    Outc(j,:)= v(:,countp)';
end
%回想結(jié)束
Outc