1、精選優(yōu)質(zhì)文檔-傾情為你奉上精選優(yōu)質(zhì)文檔-傾情為你奉上專心-專注-專業(yè)專心-專注-專業(yè)精選優(yōu)質(zhì)文檔-傾情為你奉上專心-專注-專業(yè)應用BP神經(jīng)網(wǎng)絡逼近非線性函一、實驗要求1、逼近的非線性函數(shù)選取為y=sin(x1)+cos(x2)，其中有兩個自變量即x1,x2,一個因變量即y。2、逼近誤差learnGoal) collectHiddenOut = logsig(HH*input_train); %計算隱含層輸出 hiddenOut = collectHiddenOut ones(361,1); networkOut = OO*hiddenOut; %計算網(wǎng)絡輸出 error = output_tr

2、ain-networkOut; %計算誤差 %利用目標函數(shù)，判斷是否結束循環(huán) aimJ = sumsqr(error) if (aimJlearnGoal) break; end %統(tǒng)計訓練次數(shù) trainNum = trainNum+1; %權值閾值調(diào)整因子 factor2 = error; factor1 = w2*factor2.*collectHiddenOut.*(1-collectHiddenOut); %調(diào)整權值和閾值調(diào)節(jié)量 dHH = factor1*input_train; dOO = factor2*hiddenOut; %權值閾值調(diào)整 if (trainNum3) HH

3、= HH + learnSpeed*dHH; OO = OO + learnSpeed*dOO; collectHH = collectHH HH; collectOO = collectOO OO; w1 = HH(:,1:inputnum); b1 = HH(:,1+inputnum); w2 = OO(:,1:hiddennum); b2 = OO(:,1+hiddennum); else %附加動量法 HH = HH + learnSpeed*dHH + 0.94*(collectHH(:,(trainNum-2)*3+1):(trainNum-2)*3+3)-collectHH(:,

4、(trainNum-3)*3+1):(trainNum-3)*3+3); OO = OO + learnSpeed*dOO + 0.94*(collectOO(1,(trainNum-2)*10+1):(trainNum-2)*10+10)-collectOO(1,(trainNum-3)*10+1):(trainNum-3)*10+10); collectHH = collectHH HH; collectOO = collectOO OO; w1 = HH(:,1:inputnum); b1 = HH(:,1+inputnum); w2 = OO(:,1:hiddennum); b2 =

5、OO(:,1+hiddennum); end %訓練數(shù)據(jù)測試，計算最大誤差率 hiddenOut_test = logsig(HH*input_train); %參數(shù)修改后的隱含層輸出 network_test = w2*hiddenOut_test+repmat(b2,1,361); %預測結果 rate = (output_train-network_test)./output_train; %誤差率 max_rate = max(abs(rate); %誤差率最大值end%顯示測試結果%標準函數(shù)圖像y=sin(x1)+cos(x2)x,y = meshgrid(-4.5:0.1:4.5,

6、-4.5:0.1:4.5);z = sin(x)+cos(y);figure(1)mesh(x,y,z)xlabel(x1);ylabel(x2);zlabel(y);%網(wǎng)絡圖t1=linspace(min(input(:,1),max(input(:,1);t2=linspace(min(input(:,2),max(input(:,2);X,Y=meshgrid(t1,t2);Z=griddata(input(:,1),input(:,2),network_test,X,Y);figure(2)mesh(X,Y,Z)xlabel(Input1);ylabel(Input2);zlabel(Output);%繪制誤差曲線t3=linspace(min(input(:,1),max(input(:,1);t4=linspace(min(input(:,2),max(input(:,2);X1,X2=meshgrid(t3,t4);E=griddata(