%BP neural network加动量项的算法
clear
inputNums=5;%输入节点数
outputNums=5;%输出节点数
hideNums=15;%隐层节点数
maxcount=10000;%最大训练次数
samplenum=7;%总样本组数
precision=0.001;%精度
alpha=0.5;%学习率设定值
a=0.5;%动量系数设定值
count=1;
error=zeros(1,count);
errorp=zeros(1,samplenum);
v=rand(inputNums,hideNums);
deltv=zeros(inputNums,hideNums);
dv=zeros(inputNums,hideNums);
st1=zeros(1,hideNums);
w=rand(hideNums,outputNums);
deltw=zeros(hideNums,outputNums);
dw=zeros(hideNums,outputNums);
st2=zeros(1,outputNums);
%samplelist=[1,0,0;0,1,0;0,0,1];
%expectlist=[1,0,0;0,1,0;0,0,1];
samplelist=[0,0.8823,0,1,0;
0.9910,0.0176,0.9230,0,0.8174;
1,1,0.9030,1,0.9588;
0.0020,0.8823,0.0437,1,0.1582;
1,0.8823,0.3569,1,0.6622;
0.9535,0.0963,0.009,0.5386,0.0593;
1,0.9983,0,1,0];
expectlist=[1,0,0,0,0,0,0,0,0;
0,1,0,0,0,0,0,0,0;
0,0,1,0,0,0,0,0,0;
0,0,0,1,0,0,0,0,0;
0,0,0,0,1,0,0,0,0;
0,0,0,0,0,1,0,0,0;
0,0,0,0,0,0,1,0,0];
while (count<=maxcount)
c=1;
while (c<=samplenum)
for k=1:outputNums
d(k)=expectlist(c,k);%获得期望输出的向量
end
for i=1:inputNums
x(i)=samplelist(c,i);%获得输入的向量(数据)
end
%Forward();
for j=1:hideNums
net=0.0;
for i=1:inputNums
net=net+x(i)*v(i,j);
end
net=net-st1(j);
y(j)=1/(1+exp(-net));
end
for k=1:outputNums
net=0.0;
for j=1:hideNums
net=net+y(j)*w(j,k);
end
net=net-st2(k);
o(k)=1/(1+exp(-net));
end
%BpError(c);
errortmp=0.0;
for k=1:outputNums
errortmp=errortmp+(d(k)-o(k))^2;
end
errorp(c)=0.5*errortmp;
%end
%Backward();
for k=1:outputNums
yitao(k)=(d(k)-o(k))*o(k)*(1-o(k));
end
for j=1:hideNums
tem=0.0;
for k=1:outputNums
tem=tem+yitao(k)*w(j,k);
end
yitay(j)=tem*y(j)*(1-y(j));
end
%调整各层权值
for j=1:hideNums
for k=1:outputNums
deltw(j,k)=alpha*yitao(k)*y(j);
w(j,k)=w(j,k)+deltw(j,k)+a*dw(j,k);
dw(j,k)=deltw(j,k);
end
end
for k=1:outputNums
st2(k)=st2(k)+alpha*yitao(k);
end
for i=1:inputNums
for j=1:hideNums
deltv(i,j)=alpha*yitay(j)*x(i);
v(i,j)=v(i,j)+deltv(i,j)+a*dv(i,j);
dv(i,j)=deltv(i,j);
end
end
for j=1:hideNums
st1(j)=st1(j)+alpha*yitay(j);
end
%end
c=c+1;
end
double tmp;
tmp=0.0;
for i=1:samplenum
tmp=tmp+errorp(i)*errorp(i);
end
tmp=tmp/c;
error(count)=sqrt(tmp);
if (error(count)<precision)
break;
end
count=count+1;%训练次数加1
end
p=1:count-1;
plot(p,error(p),'-');
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