代码搜索结果

p=[58478 135185 5.46 0.23 16.5 0.21 1005.3 585.44; 67884 152369 5.46 0.27 18.7 0.26 1105.6 575.03; 74462 182563 6.01 0.25 21.6 0.28 1204.6 601.23; 78345 201587 6.12 0.26 25.8 0.29 1316.5 627.89; 8

P=[0.1254 0.2315 0.3297; 0.2315 0.3297 0.4376; 0.3297 0.4376 0.5425; 0.4376 0.5425 0.6078; 0.5425 0.6078 0.7119]'; T=[0.4376 0.5425 0.6078 0.7119 0.8685]; P_test=[0.6078 0.7119 0

%构建训练样本中的输入向量p p1=[-0.0909 0.4825 0.9580 0.6643 0.0350 -0.6224; -0.1408 0.3944 0.9718 0.7183 0.0423 -0.6620; -0.2500 0.1250 0.9688 0.5000 0.0000 -0.0625; -0.2984 0.3037 -0.7801 0.0419 -0.3665 -0.8

P=[0.2452 0.1466 0.1314 0.2243 0.5523 0.6642 0.7015 0.6981 0.6821 0.6945 0.7549 0.8215 0.2415 0.3027 0; 0.2217 0.1581 0.1408 0.2304 0.5134 0.5312 0.6819 0.7125 0.7265 0.6847 0.7826 0.8325 0.2385

%创建一个BP网络 net = newff([-2 2],[4 1],{'tansig','purelin'},'trainlm','learngdm','msereg'); p = [-2 -1 0 1 2]; t = [0 1 1 1 0]; y = sim(net,p) %误差向量 e = t-y %设置性能参数 net.performParam.ratio = 20/(

Processor 7: 42 38 69 52 47 15 27 73 89 16 0 10 67 3 66 97 71 69 55 33 67 55 36 32 39 9 29 79 53 54 2 67 50 31 29 56 21 52 90 43 64 27 57 72 21 49 65 32 59 53 43 13 51 78 70 22 43 96 75 80 41 0 93 54