求

lyfsgdhr

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];
! N) q- f# v& }) V! }& fT=[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];
%  创建一个新的前向神经网络
net_1=newff(minmax(P),[10,1],{'tansig','purelin'},'traingdm')
) K$ W7 u/ }/ M  P  F* X%  当前输入层权值和阈值
) c4 o0 _3 j9 |7 R3 FinputWeights=net_1.IW{1,1}
inputbias=net_1.b{1}
0 F$ N& L* Q# j%  当前网络层权值和阈值
layerWeights=net_1.LW{2,1}
6 W8 i1 ~$ e0 r0 Blayerbias=net_1.b{2}
%  设置训练参数
! K7 U+ I' @! Ynet_1.trainParam.show = 50;
  C, T- q% m3 P; knet_1.trainParam.lr = 0.05;
- e0 a. F# x+ y5 D. ~net_1.trainParam.mc = 0.9;
% Y3 r9 d3 W8 I+ S9 x9 D/ Ynet_1.trainParam.epochs = 10000;
% K% v. s6 K: N) o6 Q" \/ L, Pnet_1.trainParam.goal = 1e-3;
  k7 @, x& s4 x3 {%  调用 TRAINGDM 算法训练 BP 网络
- a' y5 B! }0 I" _) @* {$ g[net_1,tr]=train(net_1,P,T);
, [& b) i( k+ k9 u- @  _%  对 BP 网络进行仿真
A = sim(net_1,P);
%  计算仿真误差
E = T - A;
MSE=mse(E)
1 [8 j" \6 s" @. A6 ^( ^5 S/ Lx=[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
sim(net_1,x)
3 Q3 l) s. z+ c) U4 k9 r2 U4 |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。