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];
T=[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];
# {4 ]9 p& [0 \* _%  创建一个新的前向神经网络
+ \! q# @/ S) p& M1 c  @& mnet_1=newff(minmax(P),[10,1],{'tansig','purelin'},'traingdm')
%  当前输入层权值和阈值
# z8 t7 @  r- u3 w: uinputWeights=net_1.IW{1,1}
inputbias=net_1.b{1}
%  当前网络层权值和阈值
layerWeights=net_1.LW{2,1}
layerbias=net_1.b{2}
%  设置训练参数
+ M! N6 m( E8 a, [7 g5 Knet_1.trainParam.show = 50;
6 w$ p* I, @# }* E: f& A/ Bnet_1.trainParam.lr = 0.05;
1 l- |% L: H' p, e& s& M" Xnet_1.trainParam.mc = 0.9;
# t) r! _6 d8 |+ H" N( q3 A7 cnet_1.trainParam.epochs = 10000;
, Y& _9 c/ h8 K) e5 i3 a# lnet_1.trainParam.goal = 1e-3;
%  调用 TRAINGDM 算法训练 BP 网络
[net_1,tr]=train(net_1,P,T);
%  对 BP 网络进行仿真
A = sim(net_1,P);
3 d' [- L/ H4 ]& L4 Z%  计算仿真误差
, f/ x( T% {. ~& U! dE = T - A;
MSE=mse(E)
8 B2 S0 M! m3 R" w  kx=[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)
% x3 k" x* ~5 z' c0 ~# q& d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 。
4 P8 e  U8 Z/ U

小青菜

看上去好烦哦

zhou2yong888

看不懂·····

fffffffddf

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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。