BP神经网络在质量控制中的应用

1、 欢迎阅读本文档，希望本文档能对您有所帮助！ 欢迎阅读本文档，希望本文档能对您有所帮助！ 感谢阅读本文档，希望本文档能对您有所帮助！ 感谢阅读本文档，希望本文档能对您有所帮助！ 欢迎阅读本文档，希望本文档能对您有所帮助！ 感谢阅读本文档，希望本文档能对您有所帮助！BP神经网络在质量控制中的应用BP神经网络简介（原理及作用） BP神经网络是一种按误差逆传播算法训练得到的多层前馈神经网络。BP神经网络能学习和存贮大量的输入-输出模式映射关系，但是无必事前揭示此种映射关系的数学方程。BP神经网络的学习方法是最速下降法，通过反向传播来不断调整神经网络的权值与阈值，使BP神经网络的误差平方和最小。BP神

2、经网络模型拓扑结构由输入层、隐层与输出层构成。BP神经网络在自相关过程残差控制图中的应用收集生成反应自相关生产过程的平稳时间序列ARMA（p，q）数据；-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用自相关或偏自相函数判断其自相性；自相关函数: C3 滞后 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 的自相关 自相关函数拖尾偏自相关函数: C3 滞后 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 的偏自相关 偏自相关拖尾用神经网络建立过程的时间序列模型并计算出残差序列程序如下：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); %归一化处理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(原时间序列“+”与预测时间序列“*”)plot(1:length(T),T,+-,1:length(t1),t1,-*)e=T-t1e=efigure(2);%title(残差时间序列)plot(1:length(T),e,+-)figure(3); title(残差

10、直方图)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软件作残差序列的均值极差控制图BP神经网络在过程运行状态监测中的应用,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