Cnn_train_rollingball_滚动轴承_故障分类_cwru_神经网络_train

clc clear all close all P=[2.1329 2.2364 4.2143 3.5392 3.7349 0.5658 0.6537 0.2496; 1.4237 1.4414 2.9041 2.1076 2.8138 0.4337 0.5145 0.2045; 1.1423 1.1917 2.4049 1.5040 2.0923 0.2884 0.3256 0.1941; 0.9641 1.1029 2.1104 0.8997 1.7167 0.2206 0.2368 0.0766; 1.9623 2.2138 4.1505 3.0073 3.5051 0.4962 0.5759 0.2091; 2.0554 2.2913 4.4578 3.0064 3.9799 0.5954 0.6961 0.2454; 1.1887 1.3242 2.5919 1.5149 2.1879 0.2777 0.3156 0.1572; 1.1762 1.3840 2.6347 1.1066 1.9657 0.2362 0.2576 0.0879; 1.8830 2.1891 4.1069 2.7970 3.3061 0.4733 0.5176 0.1941; 1.8058 1.8210 3.5942 2.7796 3.5514 0.5728 0.6781 0.2457; 1.2113 1.3324 2.6267 1.4499 2.2559 0.3095 0.3314 0.1556; 1.3627 1.5622 2.9368 1.3071 2.5098 0.3354 0.3522 0.1224; 1.7466 2.0496 3.8267 2.6054 3.2447 0.4486 0.5106 0.1703; 1.4933 1.4985 3.0207 2.1467 2.9203 0.4630 0.5274 0.2112; 1.2735 1.4002 2.7454 1.4823 2.3844 0.3229 0.3555 0.1697; 1.3343 1.5561 2.9557 1.2760 2.4284 0.3229 0.3430 0.1352; 1.5815 1.8360 3.4809 2.3606 2.9496 0.4285 0.4715 0.1648; 1.8699 1.8815 3.7590 2.7671 3.5261 0.5457 0.6394 0.2252; 1.3163 1.5043 2.9163 1.5546 2.4345 0.3281 0.3641 0.1492; 1.3553 1.5763 2.9829 1.3260 2.5040 0.3378 0.3549 0.1300; 1.5207 1.7405 3.3100 2.2001 2.7955 0.4025 0.4367 0.1663; 1.8146 1.9059 3.7568 2.5816 3.4864 0.5124 0.6037 0.2237; 1.4310 1.5784 3.1111 1.6645 2.6151 0.3571 0.3919 0.1700; 1.2870 1.5201 2.8666 1.2610 2.4018 0.3113 0.3381 0.1202; 1.5815 1.7899 3.4642 2.2858 2.8504 0.4154 0.4544 0.1654; 1.9340 2.0707 4.0968 2.8013 3.6772 0.5457 0.6308 0.2487; 1.4386 1.5717 3.0683 1.6974 2.9444 0.4154 0.4526 0.1471; 1.2601 1.4811 2.8089 1.2052 2.2596 0.2844 0.3122 0.1175; 1.6358 1.8754 3.6573 2.4583 3.0403 0.4724 0.4941 0.1807; 1.6132 1.6315 3.3125 2.4287 3.1614 0.4642 0.5588 0.1978; 1.4972 1.6575 3.2191 1.7270 3.0686 0.4321 0.4660 0.1590; 1.2058 1.4069 2.6514 1.1328 2.1747 0.2777 0.3092 0.1047; 2.0237 2.2187 4.2314 3.1233 3.7709 0.5866 0.6571 0.2365; 1.8513 1.9062 3.8231 2.6048 3.5560 0.5457 0.6333 0.2384; 1.4628 1.6416 3.1999 1.6712 2.9163 0.4068 0.4456 0.1505; 1.2452 1.4615 2.7479 1.1670 2.2211 0.2875 0.3146 0.1315]'; u=(P-min(min(P)))./(max(max(P))-min(min(P))); T=[0 0 0 1; 0 0 1 0;0 1 0 0;1 0 0 0; 0 0 0 1; 0 0 1 0;0 1 0 0;1 0 0 0; 0 0 0 1; 0 0 1 0;0 1 0 0;1 0 0 0; 0 0 0 1; 0 0 1 0;0 1 0 0;1 0 0 0; 0 0 0 1; 0 0 1 0;0 1 0 0;1 0 0 0; 0 0 0 1; 0 0 1 0;0 1 0 0;1 0 0 0; 0 0 0 1; 0 0 1 0;0 1 0 0;1 0 0 0; 0 0 0 1; 0 0 1 0;0 1 0 0;1 0 0 0; 0 0 0 1; 0 0 1 0;0 1 0 0;1 0 0 0]'; tic net=newff(minmax(u),[8,4],{'tansig','logsig'},'trainlm'); net.trainParam.epochs=2000; net.trainParam.goal=0.001; LP.lr=0.1; net=train(net,u,T); net.iw{1} net.b{1} net.lw{2} net.b{2} toc P_test=[1.9187 2.1567 4.1450 3.8751 3.6738 0.5530 0.6226 0.2255; 1.9349 1.9965 3.9924 2.6737 3.6473 0.5490 0.6494 0.2460; 1.1423 1.1717 2.4049 1.5240 2.0923 0.2884 0.3556 0.1971; 1.2268 1.4325 2.7274 1.1710 2.3054 0.3049 0.3357 0.1199]'; u_test=(P_test-min(min(P_test)))./(max(max(P_test))-min(min(P_test))); T_test=[0 0 0 1; 0 0 1 0;0 1 0 0;1 0 0 0]'; Y=sim(net,u_test); error=abs(T_test-Y); subplot(2,2,1),bar(1:4,error(:,1)); subplot(2,2,2),bar(1:4,error(:,2)); subplot(2,2,3),bar(1:4,error(:,3)); subplot(2,2,4),bar(1:4,error(:,4));