examofrbf1.m

RBF神经网络应用于IRIS数据集的例子

SamNum=100;
TestSamNum=101;
InDim=1;
ClusterNum=10;
lr=0.001;

%根据目标函数获得样本输入输出
rand('state',sum(100*clock));  %Resets it to a different state each time.
NoiseVar=0.1;
Noise=NoiseVar*randn(1,SamNum);
SamIn=8*rand(1,SamNum)-4;
SamOutNoNoise=1.1*(1-SamIn+2*SamIn.^2).*exp(-0.5*SamIn.^2);
SamOut=SamOutNoNoise;

TestSamIn=-4:0.08:4;
TestSamOut=1.1*(1-TestSamIn+2*TestSamIn.^2).*exp(-0.5*TestSamIn.^2);

figure(1)
grid
plot(SamIn,SamOut,'k+')
xlabel('Input x')
ylabel('Output y')

figure(2)
grid
plot(TestSamIn,TestSamOut,'k-')
xlabel('Input x')
ylabel('Output y')

figure(3)
grid
hold on
plot(SamIn,SamOut,'k+')
plot(TestSamIn,TestSamOut,'k-')
xlabel('Input x')
ylabel('Output y')
hold off

x=Centers=-4*rand(1,10);
y=Spreads=0.2+0.1*rand(10,1);
z=Weights=0.1*rand(1,10);

error=zeros(1,SamNum);
NNOut=zeros(1,SamNum);

for k=1:SamNum
    for i=1:ClusterNum
        NNOut(k)=NNOut(k)+Weights(i)*dist(SamIn(k),Centers(i));
    end
    error(k)=SamOut(k)-NNOut(k);
end

aimfuct=0.5*sum(error.^2);
gradf=jacobian(aimfuct);
deltaCenters=-lr*



NumberInClusters=zeros(ClusterNum,1);
IndexInClusters=zeros(ClusterNum,SamNum);
while 1
    NumberInClusters=zeros(ClusterNum,1);
    IndexInClusters=zeros(ClusterNum,SamNum);
    
    for i=1:SamNum
        AllDistance=dist(Centers',SamIn(i));
        [MinDist,Pos]=min(AllDistance);
        NumberInClusters(Pos)=NumberInClusters(Pos)+1;
        IndexInClusters(Pos,NumberInClusters(Pos))=i;
    end
    
    OldCenters=Centers;
    
    for i=1:ClusterNum
        Index=IndexInClusters(i,1:NumberInClusters(i));
        Centers(:,i)=mean(SamIn(:,Index)')';
    end
    
    EqualNum=sum(sum(Centers==OldCenters));
    if EqualNum==InDim*ClusterNum
        break
    end
end

AllDistances=dist(Centers',Centers);
Maximum=max(max(AllDistances));
for i=1:ClusterNum
    AllDistances(i,i)=Maximum+1;
end
Spreads=Overlap*min(AllDistances)';

Distance=dist(Centers',SamIn);
SpreadsMat=repmat(Spreads,1,SamNum);
HiddenUnitOut=radbas(Distance./SpreadsMat);
HiddenUnitOutEx=[HiddenUnitOut' ones(SamNum,1)]';
W2Ex=SamOut*pinv(HiddenUnitOutEx);
W2=W2Ex(1:ClusterNum);
B2=W2Ex(ClusterNum+1);


TestDistance=dist(Centers',TestSamIn);
TestSpreadsMat=repmat(Spreads,1,TestSamNum);
TestHiddenUnitOut=radbas(TestDistance./TestSpreadsMat);
TestNNOut=W2*TestHiddenUnitOut+B2;
figure(4)
plot(TestSamIn,TestNNOut,'k-');
display(W2);
display(B2);
figure(5)
hold on 
plot(TestSamIn,TestNNOut-TestSamOut,'r-');
plot(TestSamIn,TestSamOut,'c-')
plot(TestSamIn,TestNNOut,'b-');
hold off