ran.m

神经网络的资源分配网算法

function ran()

TrainSamNum=400;   %训练样本数
TestSamNum=201;    %测试样本数
InDim=1;           %样本输入维数
OutDim=1;          %样本输出维数
%根据目标函数获得样本输入输出
TrainSamIn=4*rands(1,TrainSamNum);
TrainSamOut=1.1*(1-TrainSamIn+2*TrainSamIn.^2).*exp(-TrainSamIn.^2/2);
TestSamIn=-4:0.04:4;
TestSamOut=1.1*(1-TestSamIn+2*TestSamIn.^2).*exp(-TestSamIn.^2/2);
TestSTD=std(TestSamOut);

OverLapCoe=0.87;%重叠系数
Dist_Max=2.0;%最大距离分辨率
Dist_Min=0.2;%最小距离分辨率
ErrLimit=0.02;%误差分辨率
Decay=0.977;%分辨率衰减常数
lr=0.05;%学习率
MaxEpoch=100;%最大学习次数
DistLimit=Dist_Max;%距离分辨率
b2=TrainSamOut(:,1);
w2=[];
UnitCenters=[];
SpreadConstant=[];
UnitNum=0;
AllUnitNum=0;
AllTestRSME=[];
tp=[ErrLimit lr MaxEpoch];
for TrainedNum=2:TrainSamNum
    TrainedNum
    NewInput=TrainSamIn(:,TrainedNum);
    NewOutput=TrainSamOut(:,TrainedNum);
    NetOut=RBFNN(NewInput,UnitCenters,w2,b2,SpreadConstant);
    NewErr=NewOutput-NetOut;
 
    if(UnitNum==0)
        NewDist=Dist_Max;
    else
        AllDist=dist(UnitCenters',NewInput)
        NewDist=min(AllDist);
    end
    
    if(norm(NewErr)>=ErrLimit&NewDist>=DistLimit)     %判断是否加新节点
        [UnitCenters,w2,SpreadConstant]
        AddNewUnit(NewInput,NewErr,NewDist,UnitCenters,w2,SpreadConstant,OverLapCoe);
        UnitNum=UnitNum+1;
    else
        [UnitCenters,w2,b2,SpreadConstant]
        FineTuning(NewInput,NewOutput,UnitCenters,w2,b2,SpreadConstant,tp);
    end
    
    if DistLimit>Dist_Min     %分辨率衰减
        DistLimit=DistLimit*Decay;
    else
        DistLimit=Dist_Min;
    end
    
    AllUnitNum=[AllUnitNum UnitNum];
    
    TestNNOut=RBFNN(TestSamIn,UnitCenters,w2,b2,SpreadConstant);
    TestRSME=sqrt(sumsqr(TestNNOut-TestSamOut)/TestSamNum)/TestSTD;
    AllTestRSME=[AllTestRSME TestRSME];
end

%绘制目标曲线和神经网络输出曲线
TestNNOut=RBFNN(TestSamIn,UnitCenters,w2,b2,SpreadConstant);
[xxx,PtNum]=size(TestSamOut);
figure
echo off
axis([0 PtNum -0.2 3.0])
axis on 
grid
hold on 
plot(1:PtNum,TestSamOut,'b-')
plot(1:PtNum,TestNNOut,'k-')
UnitNum
TestRSME

%绘制隐藏层节点变化曲线
[xxx,PtNum]=size(AllUnitNum);
figure
echo off
axis([0 PtNum 0 40])
axis on
grid
hold on
plot(1:PtNum,AllUnitNum,'b-')

%绘制RSME变化曲线
[xxx,PtNum]=size(AllTestRSME);
figure
echo off
axis on
grid
hold on
plot(1:PtNum,AllTestRSME,'b-');

function NetOut=RBFNN(NewInput,UnitCenters,w2,b2,SpreadConstant)
[UnitNum,OutDim]=size(w2);
[xxx,InNum]=size(NewInput);
if(UnitNum==0)
    NetOut=repmat(b2,1,InNum);
else
    SpreadMat=repmat(SpreadConstant,1,InNum);
    AllDist=dist(UnitCenters',NewInput);
    a1=radbas(AllDist./SpreadMat);
    NetOut=w2*a1+b2;
end

%增加新隐节点
function [UnitCenters,w2,b2,SpreadConstant]=AddNewUnit(NewInput,NewErr,NewDist,UnitCenters,w2,SpreadConstant,OverLapCoe)
UnitCenters=[UnitCenters  NewInput];
w2=[w2 NewErr];
SpreadConstant=[SpreadConstant;OverLapCoe*NewDist];

%梯度法实现参数精调
function [UnitCenters,w2,b2,SpreadConstant]=FineTuning(NewInput,NewOutput,UnitCenters,w2,b2,SpreadConstant,tp)
[xxx,UnitNum]=size(UnitCenters);
if(UnitNum==0),b2=NewOutput;return,end
ErrLimit=tp(1);
lr=tp(2);
MaxEpoch=tp(3);
for epoch=1:MaxEpoch
    AllDist=dist(UnitCenters',NewInput);
    NewDist=min(AllDist);
    a1=radbas(AllDist./SpreadConstant);
    NetOut=w2*a1+b2;
    NewErr=NewOutput-NetOut;
    if(norm(NetErr)<ErrLimit),break,end
    b2=b2+lr*NewErr;
    w2=w2+lr*NewErr*a1';
    for i=1:UnitNum
        CentInc=2*(NewInput-UnitCenters(:,i))*a1(i)*NewErr*w2(i)/(SpreadConstant(i)^2);
        UnitCenters(:,i)=UnitCenters(:,i)+lr*CentInc;
    end
end