1、 歡迎閱讀本文檔，希望本文檔能對(duì)您有所幫助！ 歡迎閱讀本文檔，希望本文檔能對(duì)您有所幫助！ 感謝閱讀本文檔，希望本文檔能對(duì)您有所幫助！ 感謝閱讀本文檔，希望本文檔能對(duì)您有所幫助！ 歡迎閱讀本文檔，希望本文檔能對(duì)您有所幫助！ 感謝閱讀本文檔，希望本文檔能對(duì)您有所幫助！BP神經(jīng)網(wǎng)絡(luò)在質(zhì)量控制中的應(yīng)用BP神經(jīng)網(wǎng)絡(luò)簡(jiǎn)介（原理及作用） BP神經(jīng)網(wǎng)絡(luò)是一種按誤差逆?zhèn)鞑ニ惴ㄓ?xùn)練得到的多層前饋神經(jīng)網(wǎng)絡(luò)。BP神經(jīng)網(wǎng)絡(luò)能學(xué)習(xí)和存貯大量的輸入-輸出模式映射關(guān)系，但是無(wú)必事前揭示此種映射關(guān)系的數(shù)學(xué)方程。BP神經(jīng)網(wǎng)絡(luò)的學(xué)習(xí)方法是最速下降法，通過(guò)反向傳播來(lái)不斷調(diào)整神經(jīng)網(wǎng)絡(luò)的權(quán)值與閾值，使BP神經(jīng)網(wǎng)絡(luò)的誤差平方和最小。BP神

2、經(jīng)網(wǎng)絡(luò)模型拓?fù)浣Y(jié)構(gòu)由輸入層、隱層與輸出層構(gòu)成。BP神經(jīng)網(wǎng)絡(luò)在自相關(guān)過(guò)程殘差控制圖中的應(yīng)用收集生成反應(yīng)自相關(guān)生產(chǎn)過(guò)程的平穩(wěn)時(shí)間序列ARMA（p，q）數(shù)據(jù)；-1.023-0.63130.54550.74621.5147-2.78280.06740.49922.1740.4534-1.1840.79690.9540.99670.56761.85910.20710.11520.4578-0.86640.0471.2435-0.6293-0.19361.7261.8451-0.71490.09110.06680.8882.07131.3391-0.23-0.1035-1.12571.82751.6394

3、1.83921.6784-2.71960.95221.38250.5763-0.0648-0.33260.77662.1396-0.34420.58140.0409-0.32771.70050.12930.45080.5211-0.73381.49662.0064-2.4923-0.03360.6997-0.45632.45410.60480.31911.2991-0.56830.15571.14520.18382.7908-1.31320.70520.21590.42160.6586-1.63470.8457-2.16791.36411.3598-0.23912.8792-1.28361.0

4、9142.3656-0.19891.56380.2758-0.04613.3048-0.16873.03670.9583-0.21120.79390.8281.3053-0.02491.6898用自相關(guān)或偏自相函數(shù)判斷其自相性；自相關(guān)函數(shù): C3 滯后 ACF T LBQ 1 0.402804 4.03 16.72 2 0.101053 0.88 17.78 3 0.013824 0.12 17.80 4 0.103236 0.89 18.93 5 0.010944 0.09 18.94 6 -0.085554 -0.73 19.74 7 -0.119216 -1.01 21.30 8 -0.

5、077815 -0.66 21.97 9 0.039590 0.33 22.14 10 0.144187 1.21 24.50 11 0.266638 2.20 32.65 12 0.101412 0.80 33.84 13 -0.102219 -0.80 35.07 14 -0.114110 -0.89 36.61 15 -0.036642 -0.28 36.77 16 -0.124414 -0.96 38.65 17 -0.236740 -1.81 45.54 18 -0.194370 -1.44 50.24 19 -0.169470 -1.23 53.85 20 -0.181176 -1

6、.30 58.04 21 -0.021112 -0.15 58.10 22 0.031273 0.22 58.23 23 -0.005770 -0.04 58.23 24 -0.084652 -0.60 59.19 25 -0.050194 -0.35 59.53 C3 的自相關(guān) 自相關(guān)函數(shù)拖尾偏自相關(guān)函數(shù): C3 滯后 PACF T 1 0.402804 4.03 2 -0.073050 -0.73 3 -0.000516 -0.01 4 0.125802 1.26 5 -0.095023 -0.95 6 -0.075121 -0.75 7 -0.049732 -0.50 8 -0.0256

7、13 -0.26 9 0.100444 1.00 10 0.129852 1.30 11 0.212013 2.12 12 -0.101057 -1.01 13 -0.190027 -1.90 14 -0.053501 -0.54 15 -0.030070 -0.30 16 -0.105012 -1.05 17 -0.069409 -0.69 18 0.014053 0.14 19 -0.126045 -1.26 20 -0.185761 -1.86 21 0.090942 0.91 22 -0.042419 -0.42 23 -0.023667 -0.24 24 0.012443 0.12

8、25 -0.001695 -0.02 C3 的偏自相關(guān) 偏自相關(guān)拖尾用神經(jīng)網(wǎng)絡(luò)建立過(guò)程的時(shí)間序列模型并計(jì)算出殘差序列程序如下：clear all;clc;x=randn(100,1);y=zeros(100,1);a(1)=0.2;a(2)=0.6;b=0.3;y(1)=a(1)+x(1);for n=2:100 y(n)=a(1)+a(2)*y(n-1)+x(n)-b*x(n-1);endyfor j=1:100 y(j)=(y(j)-min(y)/(max(y)-min(y); %歸一化處理endfor i=1:97 P(:,i)=y(i),y(i+1),y(i+2)end T=y(4:1

9、00)T=Tnet=newff(0 1;0 1;0 1,5,1,tansig,logsig,traingd);net.trainParam.epochs=15000;net.trainParam.goal=0.01;net.trainParam.lr=0.1;net=train(net,P,T); figure(1);title(原時(shí)間序列“+”與預(yù)測(cè)時(shí)間序列“*”)plot(1:length(T),T,+-,1:length(t1),t1,-*)e=T-t1e=efigure(2);%title(殘差時(shí)間序列)plot(1:length(T),e,+-)figure(3); title(殘差

10、直方圖)histfit(e)0.2366-0.1103-0.13470.0955-0.2325-0.20220.0982-0.2208-0.05280.4290.1373-0.31460.04550.0304-0.0720.04210.141-0.1098-0.033-0.26720.1186-0.01180.25910.3786-0.528-0.02440.0773-0.1636-0.1394-0.3301-0.03340.2119-0.21170.1149-0.2868-0.19850.1060.01630.01220.1711-0.18020.1020.2793-0.5816-0.195

11、90.1525-0.24150.20050.36760.00480.0551-0.1765-0.13660.0093-0.34290.2826-0.2238-0.01470.02840.082-0.1092-0.1699-0.0041-0.4990.42020.05450.0910.3523-0.0172-0.06470.1789-0.04880.038-0.0458-0.54320.3439-0.08450.37450.1863-0.3494-0.03010.10560.1167-0.03590.3699-0.2519-0.0590.09070.3310.061-0.05090.3782-0

12、.04710.05660.04780.13330.1134、用minitab軟件作殘差序列的均值極差控制圖BP神經(jīng)網(wǎng)絡(luò)在過(guò)程運(yùn)行狀態(tài)監(jiān)測(cè)中的應(yīng)用,程序如下：clear all;clc;p=load(p.txt)p=pt=load(t.txt);t=t net=newff(minmax(p),13,5,tansig,purelin,trainlm);net.trainParam.epochs=1500;net.trainParam.goal=0.01;net.trainParam.lr=0.1;net=train(net,p,t); t1=sim(net,p)t1=sim(net,p)IW=ne

13、t.IW1,1 LW=net.LW2,1 b1=net.b1,1 b2=net.b2,1max_t1,index=max(abs(t1)a=indexmax_t,index=max(t)b=indexc=a-bIW = 0.0640 -0.5101 0.2478 -0.4403 -0.8649 0.1896 0.0006 -0.1196 0.0979 -0.0657 -0.2098 0.0668 -0.0053 0.0410 -0.0702 0.8421 0.3494 -0.0937 0.0094 -0.1322 -0.5518 0.1521 -0.1917 0.2766 0.0013 0.0

14、185 -0.2406 -0.7263 -0.0901 0.0174 -0.0009 -0.0356 0.8021 0.3594 -0.2108 -0.0675 -0.0024 0.0267 0.1138 -1.5195 0.2918 -0.0469 0.0044 -0.0443 -0.0628 0.2705 -0.4481 0.0552 -0.0037 0.0238 1.1816 0.1243 -1.0187 -0.2500 -0.0052 0.0371 0.0803 0.1021 0.3516 -0.0855 -0.0055 0.0768 0.0038 -1.2757 0.7139 -0.

15、0433 0.0068 -0.0272 0.3743 -0.1718 0.3354 -0.3575 0.0344 -0.2047 -0.0930 0.2048 -0.2176 -0.1199LW = Columns 1 through 9 0.1376 0.0869 -0.1398 0.4112 -0.2075 0.0110 -0.0962 0.5371 0.0239 -0.1376 0.0611 1.3396 -0.5504 0.4445 0.9519 0.4120 0.0603 0.0097 0.3503 -0.1868 -0.9021 0.1470 0.3105 -1.0563 0.8960 -1.0719 -1.5100 0.0572 0.1904 -0.3101 -1.2424 0.1771 0.1718 -1.0742 -0.3235 1.5496 -0.1858 -0.5161 -0.0474 -0.1411 -0.3059 0.0009 0.0243 -0.2128 -0.0040 Columns 10 through 13 0.4351 0.2808 -0.3877 0.2027 -1.4555 0.0508