是我先训练一个神经网络，训练完之后就输入了这段代码：我的完全源程序如下；>> PP =[-1.0000, -0.9714, -0.9429, -0.9143, -0.8857, -0.8571, -0.8286, -0.8000, -0.7714,-0.7429,-0.7143, -0.6857,-0.6571,-0.6286,-0.6000, -0.5714,-0.5429,-0.5143,-0.4857,-0.4571,-0.4286,-0.4000,-0.3714,-0.3429, -0.3143,-0.2857,-0.2571,-0.2286,-0.2000, -0.1714,-0.1429,-0.1143, -0.0857,-0.0571,-0.0286, 0,0.0286,0.0571, 0.0857,0.1143,0.1429,0.1714, 0.2000, 0.2286,0.2571,0.2857, 0.3143,0.3429,0.3714,0.4000,0.4286,0.4571,0.4857,0.5143,0.5429,0.5714,0.6000,0.6286,0.6571,0.6857, 0.7143,0.7429,0.7714, 0.8000,0.8286,0.8571,0.8857, 0.9143, 0.9429, 0.9714, 1.0000; -1.0000,-0.9714,-0.9429, -0.9143,-0.8857,-0.8571,-0.8286,-0.8000,-0.7714,-0.7429,-0.7143,-0.6857,-0.6571,-0.6286,-0.6000,-0.5714,-0.5429,-0.5143,-0.4857,-0.4571, -0.4286,-0.4000,-0.3714,-0.3429,-0.3143, -0.2857,-0.2571,-0.2286,-0.2000,-0.1714,-0.1429,-0.1143,-0.0857,-0.0571,-0.0286, 0,0.0286, 0.0571,0.0857,0.1143, 0.1429, 0.1714, 0.2000, 0.2286, 0.2571, 0.2857, 0.3143, 0.3429,0.3714, 0.4000, 0.4286,0.4571, 0.4857,0.5143,0.5429,0.5714,0.6000,0.6286,0.6571,0.6857,0.7143,0.7429,0.7714, 0.8000, 0.8286,0.8571,0.8857,0.9143,0.9429,0.9714,1.0000; -1.0000,-1.0000,-1.0000,-1.0000,-1.0000,-1.0000,-1.0000, -1.0000,-1.0000,-1.0000, -1.0000,-1.0000,-1.0000,-1.0000,-1.0000,-1.0000,-1.0000,-1.0000,-1.0000,-1.0000,-1.0000,-1.0000, -1.0000, -1.0000,-1.0000,1.0000,1.0000,1.0000, 1.0000, 1.0000, 1.0000,1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000,1.0000,1.0000,1.0000, 1.0000, 1.0000, 1.0000, 1.0000,1.0000,1.0000,1.0000,1.0000,1.0000, 1.0000, 1.0000, 1.0000,1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000,1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000;-1.0000,-0.9836,-0.9672,-0.9508,-0.9344,-0.9180,-0.9016, -0.8852,-0.8689, -0.8525,-0.8361, -0.8164,-0.7967, -0.7770,-0.7508, -0.7377,-0.7246, -0.7115, -0.6984, -0.6852, -0.6721, -0.6131, -0.5541,-0.4951,-0.4361,-0.3770,-0.3180, -0.2590,-0.2328, -0.1738,-0.0820, -0.0623,-0.0426, -0.0230,-0.0033, 0.0164, 0.0361, 0.0557, 0.0754, 0.0951, 0.1148, 0.1279, 0.1410, 0.1541, 0.1672, 0.1803, 0.2000, 0.2197,0.2393, 0.2590,0.2787, 0.2984,0.3180, 0.3377, 0.3574,0.3770, 0.3967, 0.4164, 0.4361, 0.4557, 0.4754, 0.5475, 0.6197, 0.6918, 0.7639, 0.8361, 0.8689, 0.9016, 0.9344, 0.9672, 1.0000];
TT =[0.2930, 0.1687,0.1027, 0.1465,0.1458,0.1190, 0.1589, 0.1511,0.1452, 0.1393,0.1334, 0.1288,0.1249, 0.1210, 0.1177, 0.1151,0.1131, 0.1105,0.1092, 0.1073, 0.1060,0.1040,0.1040,0.1014, 0.1001,0.1962,0.1877,0.1799, 0.1733, 0.1668,0.1615, 0.1563, 0.1511, 0.1472,0.1426,0.1387,0.3950, 0.3649,0.4212, 0.3885,0.3473,0.4598, 0.4133, 0.4284,0.4061,0.4480,0.5088, 0.4663, 0.5284,0.5350, 0.4892, 0.6730,0.5638, 0.5154, 0.5801,0.6409,0.5657,0.5428, 0.6095, 0.5160, 0.5971,0.5664,0.6351,0.5762, 0.6462,0.5853,0.8725,0.8332, 0.8862, 0.7528,0.8999; 0.2944,0.3927,0.3124,0.1000,0.4430, 0.3245, 0.5110,0.5375, 0.5628,0.5879,0.6123, 0.6360, 0.6590,0.6816,0.7034,0.7248,0.7456, 0.7660, 0.7860, 0.8057,0.8250, 0.8441,0.8629,0.8816,0.9000, 0.5870,0.6074,0.6273,0.6465,0.6653,0.6837, 0.7016,0.7191,0.7363, 0.7532,0.7698, 0.5951,0.6243, 0.6026,0.6327,0.6682, 0.6214,0.6616,0.6634, 0.6892, 0.6737, 0.6476, 0.6843,0.6576, 0.6642,0.7027, 0.6074, 0.6783, 0.7178, 0.6891, 0.6645,0.7175, 0.7414, 0.7114, 0.7779,0.7379,0.7662, 0.7347, 0.7790,0.7468, 0.7921, 0.6440, 0.6725, 0.6544, 0.7318, 0.6643];
NodeNum=22;
TypeNum=2;
TF1='logsig';TF2='logsig';
net=newff(minmax(PP),[NodeNum TypeNum],{TF1,TF2},'traingd');
net.trainParam.epochs = 100000; %最大训练轮回
net.trainParam.goal = 1e-3;
net = train(net,PP,TT);
Warning: NEWFF used in an obsolete way.
> In nntobsu at 18
In newff at 86
See help for NEWFF to update calls to the new argument list.
>> fid=fopen('F:\b.txt','wt');%写入文件路径
matrix=net.iw{1,1}; %input_matrix为待输出矩阵
[m,n]=size(matrix);
for i=1:1:m
for j=1:1:n
if j==n
fprintf(fid,'%g\n',matrix(i,j));
else
fprintf(fid,'%g\t',matrix(i,j));
end
end
end