iprovedranethylene.asv

改进RAN应用股票财务程序

        resizeflag=1;
        continue;  %  执行到此不再执行循环体下面尚没有执行的部分，重新回到是否执行循环的判断
        end
    end
          
if(sse<errgoal),break,end

%  计算反向传播误差
delta2=error.*NetOut.*(1-NetOut);

%  计算权值调节量
hiddenoutex=[hiddenout' ones(TrainSamNum,1)]';
dw2ex=delta2*hiddenoutex';

%  权值调节
w2ex=w2ex+lr*dw2ex;

%  分离w2和b2
[c,d]=size(w2ex);
w2=w2ex(:,1:d-1);
b2=w2ex(:,d);
end

%  绘制学习误差曲线
figure
echo off
axis on
grid
hold on
[xx,num]=size(errhistory);
%semilogy(1:num,errhistory,'r-');  %  SEMILOGY(...) is the same as PLOT(...), except a logarithmic (base 10) scale is used for the Y-axis.
plot(1:num,errhistory,'r-');
xlabel('学习误差曲线')

%绘制目标曲线和神经网络输出曲线
TestNNOut=RBFNN(p2,UnitCenters,w2,b2,SpreadConstant);
[xxx,PtNum]=size(t2);  %此处的PtNum=201
figure
echo off  %turns off echoing.
axis([0 PtNum 0 1])
axis on  %turns axis labeling, tick marks and background back on.
%grid
hold on
plot(1:PtNum,t2,'o-')
plot(1:PtNum,TestNNOut,'k.-')
legend('Sample Outputs','HUCRAN')
xlabel('样本数')
ylabel('归一化乙烷含量')
TestRSME2=sqrt(sumsqr(TestNNOut-t2)/TestSamNum)/TestSTD






%---------------------------------------------------------------------------------------------------------
%  下面是主函数中调用的函数，均以function定义，在主函数中没有function
%程序二段中调用的函数

%  隐层输出
function hiddenout=ho(p1,UnitCenters,SpreadConstant)
[xxx,InNum]=size(p1);
SpreadMat=repmat(SpreadConstant,1,InNum);
AllDist=dist(UnitCenters',p1);
hiddenout=radbas(AllDist./SpreadMat);

%  寻找需要合并的隐节点
function [hiddenunit1,hiddenunit2]=findunittocombine(hiddencorr,hiddenvar,...
    unitscombinethreshold,biascombinethreshold)
corrtri=triu(hiddencorr)-eye(size(hiddencorr));    %  TRIU Extract upper triangular part
while(1)
    [val,pos]=max(abs(corrtri));    %  [Y,I] = MAX(X) returns the indices of the maximum values in vector I and the maximum values in Y of X
    [maxcorr,hiddenunit2]=max(val);   %对于行向量X,[c,d]=max(X),返回c,d分别表示X中最大的数及其对应X的index
    if(maxcorr<unitscombinethreshold)
        hiddenunit1=0;hiddenunit2=0;
        break  %  用以退出while循环
    end
    hiddenunit1=pos(hiddenunit2);  %  如果该语句执行则说明maxcorr>unitscombinethreshold,if段没有执行
    
    if(hiddenvar(hiddenunit1)>biascombinethreshold &...
            hiddenvar(hiddenunit2)>biascombinethreshold)
        break
    else
        corrtri(hiddenunit1,hiddenunit2)=0;
    end
end

if(hiddenunit1>0)return;end   %  return与break不同，用以退出本函数

[minvar,unit]=min(hiddenvar);
if(minvar<biascombinethreshold)
    hiddenunit1=unit;
    hiddenunit2=0;
end

%  线性回归
function [a,b]=linearreg(vect1,vect2)
[xxx,n]=size(vect1);
meanv1=mean(vect1);
meanv2=mean(vect2);
a=(vect1*vect2'/n-meanv1*meanv2)/(vect1*vect1'/n-meanv1^2);
b=meanv2-a*meanv1;

%  绘制两相关隐节点对所有样本的输出
function drawcorrelatedunitsout(unitout1,unitout2)
[xxx,ptnum]=size(unitout1);
figure
echo off
axis([0 ptnum 0 1])
axis on
grid
hold on
plot(1:ptnum,unitout1,'b-')
plot(1:ptnum,unitout2,'k-')
    
%  两个隐节点合并
function [UnitCenters,SpreadConstant,w2ex]=combinetwounits(hiddenunit1,hiddenunit2,a,b,w2ex,UnitCenters,SpreadConstant)
[xxx,biascol]=size(w2ex);                         %  biascol=h1num+1,
w2ex(:,hiddenunit1)=w2ex(:,hiddenunit1)+a*w2ex(:,hiddenunit2);      %  节点unit1与下一层节点的连接权矢量
w2ex(:,biascol)=w2ex(:,biascol)+b*w2ex(:,hiddenunit2);  %  偏移权矢量更新
UnitCenters(:,hiddenunit2)=[];
SpreadConstant(hiddenunit2,:)=[];
w2ex(:,hiddenunit2)=[];                                 %  删除隐节点unit2    %  unit2与下一层的连接权值矢量

%  绘制标准差较小的单个隐节点输出
function  drawbiasedunitout(unitout)
[xxx,ptnum]=size(unitout);
figure('position',[300 300 400 300])
echo off
axis([0 ptnum 0 1])
axis on
grid
hold on
plot(1:ptnum,unitout,'k-')

%  将隐节点合并到偏移
function [UnitCenters,SpreadConstant,w2ex]=combineunittobias(hiddenunit1,unitmean,w2ex,UnitCenters,SpreadConstant)
[xxx,biascol]=size(w2ex);
w2ex(:,biascol)=w2ex(:,biascol)+unitmean*w2ex(:,hiddenunit1);
w2ex(:,hiddenunit1)=[];
UnitCenters(:,hiddenunit1)=[];
SpreadConstant(hiddenunit1,:)=[];


%  ----------------------------------------------------------------------------------------
%  程序一段中调用的函数

%网络输出函数
function NetOut=RBFNN(NewInput,UnitCenters,w2,b2,SpreadConstant)
[OutDim,UnitNum]=size(w2);
[xxx,InNum]=size(NewInput);
if(UnitNum==0),
    NetOut=repmat(b2,1,InNum);
else
    SpreadMat=repmat(SpreadConstant,1,InNum);
    AllDist=dist(UnitCenters',NewInput);
    al=radbas(AllDist./SpreadMat);
    NetOut=w2*al+b2;
end

%增加新的隐节点
function[UnitCenters,w2,SpreadConstant]=AddNewUnit(NewInput,NewErr,NewDist,UnitCenters,w2,SpreadConstant,OverLapC