程序代碼： (代碼標記  [code]...[/code]  )

SamNum = 100; % 訓練樣本數
TargetSamNum = 101; % 測試樣本數
InDim = 1; % 樣本輸入維數
UnitNum = 10; % 隱節點數
MaxEpoch = 5000; % 最大訓練次數
E0 = 0.9; % 目標誤差
% 根據目標函數獲得樣本輸入輸出
rand('state',sum(100*clock))
NoiseVar = 0.1;
Noise = NoiseVar*randn(1,SamNum);
SamIn = 8*rand(1,SamNum)-4;
SamOutNoNoise = 1.1*(1-SamIn+2*SamIn.^2).*exp(-SamIn.^2/2);
SamOut = SamOutNoNoise + Noise;
TargetIn = -4:0.08:4;
TargetOut = 1.1*(1-TargetIn+2*TargetIn.^2).*exp(-TargetIn.^2/2);
figure
hold on
grid
plot(SamIn,SamOut,'k+')
plot(TargetIn,TargetOut,'k--')
xlabel('Input x');
ylabel('Output y');
Center = 8*rand(InDim,UnitNum)-4;
SP = 0.2*rand(1,UnitNum)+0.1;
W = 0.2*rand(1,UnitNum)-0.1;
lrCent = 0.001; % 隱節點數據中心學習系數
lrSP = 0.001; % 隱節點擴展常數學習系數
lrW = 0.001; % 隱節點輸出權值學習系數
ErrHistory = []; % 用于記錄每次參數調整后的訓練誤差
for epoch = 1:MaxEpoch
AllDist = dist(Center',SamIn);
SPMat = repmat(SP',1,SamNum);
UnitOut = radbas(AllDist./SPMat);
NetOut = W*UnitOut;
Error = SamOut-NetOut;
%停止學習判斷
SSE = sumsqr(Error)
% 記錄每次權值調整后的訓練誤差
ErrHistory = [ErrHistory SSE];
if SSE<E0, break, end
for i = 1:UnitNum
CentGrad = (SamIn-repmat(Center(:,i),1,SamNum))...
*(Error.*UnitOut(i,:)*W(i)/(SP(i)^2))';
SPGrad = AllDist(i,:).^2*(Error.*UnitOut(i,:)*W(i)/(SP(i)^3))';
WGrad = Error*UnitOut(i,:)';
Center(:,i) = Center(:,i) + lrCent*CentGrad;
SP(i) = SP(i) + lrSP*SPGrad;
W(i) = W(i) + lrW*WGrad;
end
end
% 測試
TestDistance = dist(Center',TargetIn);
TestSpreadsMat = repmat(SP',1,TargetSamNum);
TestHiddenUnitOut = radbas(TestDistance./TestSpreadsMat);
TestNNOut = W*TestHiddenUnitOut;
plot(TargetIn,TestNNOut,'k-')
% 繪制學習誤差曲線
figure
hold on
grid
[xx,Num] = size(ErrHistory);
plot(1:Num,ErrHistory,'k-');