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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];9 T; T2 f& |7 G+ R3 _( X: h, {
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];
' _3 U, B: o0 |% 创建一个新的前向神经网络
! |1 A, ]5 d5 K( C u$ T6 [. M3 ~net_1=newff(minmax(P),[10,1],{'tansig','purelin'},'traingdm')
$ { s( n6 N) X0 W0 f9 Z% 当前输入层权值和阈值 G J% f( S2 T q v" D& [2 p3 x
inputWeights=net_1.IW{1,1}- U' ^4 X5 q t1 k/ e `( S3 _
inputbias=net_1.b{1}
! X8 a2 t( C+ Z, w* _% 当前网络层权值和阈值: n: I8 @4 Z( ^( M) e- [4 T
layerWeights=net_1.LW{2,1}4 f* u! G+ {. u8 h8 K6 `
layerbias=net_1.b{2} p7 G; {: i" D) Y- R; X
% 设置训练参数
- `0 A* r3 ~& @9 X5 xnet_1.trainParam.show = 50;
B1 e& b+ X6 E! Gnet_1.trainParam.lr = 0.05;: P) U4 D3 Y0 b) K s8 x/ q% s
net_1.trainParam.mc = 0.9;. H; t1 C: A' p' e: c, y9 l
net_1.trainParam.epochs = 10000;
9 s' Y- X- {; v7 x. Z* ^net_1.trainParam.goal = 1e-3;8 @; G- g8 S9 |9 G2 ~( t! X" v; b
% 调用 TRAINGDM 算法训练 BP 网络3 m. J6 i( ?, |9 C
[net_1,tr]=train(net_1,P,T);
( B2 M" Q# ]# I7 \ g7 Q% 对 BP 网络进行仿真. b# A m' }4 v. i; W8 m4 R+ a
A = sim(net_1,P);/ c0 }4 {- P& ]% p( n$ H5 m6 D6 E
% 计算仿真误差
6 } Z- n/ |$ ^4 p0 K: }% xE = T - A;
* D& \: [" W2 g0 L4 AMSE=mse(E)+ s/ z+ l) f; p6 G6 S
x=[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& b8 p5 ?7 ^ ^
sim(net_1,x) ( S. F* z5 n$ K$ [; W- {7 A
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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zan
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