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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];" C, V+ Q4 n$ c% k
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];
: @( P& O2 i9 P( B5 Z1 n% 创建一个新的前向神经网络 , L# r. l$ Z. _) Z9 u, a
net_1=newff(minmax(P),[10,1],{'tansig','purelin'},'traingdm')- u) U5 r4 Q% s# l
% 当前输入层权值和阈值
+ f2 A2 l9 I% T/ _inputWeights=net_1.IW{1,1}
, C7 ]# ~( H) |3 x# \ Uinputbias=net_1.b{1}
- |5 d6 V8 Q% M. y+ d% 当前网络层权值和阈值0 w! q8 B( c/ c( F ?
layerWeights=net_1.LW{2,1}4 x7 v$ o K7 ]1 {
layerbias=net_1.b{2}
& T! @0 o# q4 j9 l% 设置训练参数: E5 O* B8 m. {/ k) {
net_1.trainParam.show = 50;
: d d* l! v# C# p& Unet_1.trainParam.lr = 0.05;
1 v- p0 o+ ?6 Znet_1.trainParam.mc = 0.9;
$ J: c0 U6 |5 C& v8 M! enet_1.trainParam.epochs = 10000;
- y8 {' v2 W6 U; Z4 g8 knet_1.trainParam.goal = 1e-3;5 M4 v* w1 H s
% 调用 TRAINGDM 算法训练 BP 网络7 k k3 x4 t: D! L% Q. g: g& A) }
[net_1,tr]=train(net_1,P,T);
! \6 ?+ w# v0 J6 G7 W! ?- D% 对 BP 网络进行仿真
! n6 a% |; P& y" @$ U6 X2 t' D- MA = sim(net_1,P);1 a) D, G9 }$ y/ `+ M; D
% 计算仿真误差 " A# j$ G0 T- i6 [
E = T - A;
9 d0 }' e; s. B( H9 v+ {MSE=mse(E)
9 J' `' N# w, e7 i" I, yx=[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! B9 U! r, r9 {5 o3 q
sim(net_1,x)
4 Y, T! ]1 Z2 E; J7 R# X% {1 U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 。
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