求

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P=[0.1093,0.1110,0.1127,0.1141,0.1154,0.1164,0.1171,0.1175,0.1178,0.1179,0.1179,0.1179,0.1179,0.1180,0.1182];
1 u. M# u2 L8 V) n3 a% d) R5 OT=[0.1110,0.1127,0.1141,0.1154,0.1164,0.1171,0.1175,0.1178,0.1179,0.1179,0.1179,0.1179,0.1180,0.1182,0.1185];
0 ^* X) M2 h  x%  创建一个新的前向神经网络
, L- H3 T9 _$ G0 \net_1=newff(minmax(P),[10,1],{'tansig','purelin'},'traingdm')
& A! N* E$ c. _+ \6 G5 i4 G%  当前输入层权值和阈值
inputWeights=net_1.IW{1,1}
inputbias=net_1.b{1}
( x  x+ I3 I+ C" l. P%  当前网络层权值和阈值
7 O' W6 e: N8 k; d  |7 A- u- \layerWeights=net_1.LW{2,1}
layerbias=net_1.b{2}
%  设置训练参数
net_1.trainParam.show = 50;
net_1.trainParam.lr = 0.05;
7 H* }" t% w  L. z' |% u- ~net_1.trainParam.mc = 0.9;
net_1.trainParam.epochs = 10000;
6 ~$ P5 K  j7 z3 d8 c; `% w" n  Unet_1.trainParam.goal = 1e-3;
4 x# u9 w0 O0 O8 {%  调用 TRAINGDM 算法训练 BP 网络
[net_1,tr]=train(net_1,P,T);
- ^1 |8 C. w( T# ~  A% W%  对 BP 网络进行仿真
A = sim(net_1,P);
  g& F9 I: ^9 v, D! \%  计算仿真误差
- z% @% j$ a! t) L* {E = T - A;
MSE=mse(E)
( m4 I1 s3 P/ D5 [6 j! X0 sx=[0.1110,0.1127,0.1141,0.1154,0.1164,0.1171,0.1175,0.1178,0.1179,0.1179,0.1179,0.1179,0.1180,0.1182,0.1185]';%测试
8 X) U, s5 b0 a  K9 Isim(net_1,x)
这段程序是根据14年的数据，来预测下一年的，怎么算不出来啊 。

小青菜

看上去好烦哦

zhou2yong888

看不懂·····

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...........................................

kxhu

哦～～

hbchina99

鉴定完毕！  

ask4kevin

看起来好~~像啊~~~~~

talent212121

鉴定完毕！  

hyxie616

楼主，你写得实在是太好了。我惟一能做的，就只有把这个帖子顶上去这件事了

pankg

我基本上是采用看英语文章的办法，先泛读，再精读，再一句一句看，最后再提纲挈领，总算是明白一点了，当然，也可能还是领悟错了。最后要说的一句话是：楼主，你很牛叉，希望你不是真的有病。