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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];4 P8 P7 ?' K% q- p. U3 Z3 w
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];5 Q) a9 a3 s# L& \
% 创建一个新的前向神经网络 & o3 t+ q) Z( [. v1 w4 d
net_1=newff(minmax(P),[10,1],{'tansig','purelin'},'traingdm')5 t9 Y- L9 E9 Y6 V: {$ X1 h
% 当前输入层权值和阈值7 d4 z3 n, x- `
inputWeights=net_1.IW{1,1}
3 c/ c L9 ?1 H- @* g9 j3 Iinputbias=net_1.b{1}
9 S% ^7 Y% [4 }. w; ^% 当前网络层权值和阈值$ t* E; J# @/ Z7 t7 P
layerWeights=net_1.LW{2,1}
9 Y$ \( O- t" @1 f: h" slayerbias=net_1.b{2}
+ k; M! g' M) P8 v$ a% 设置训练参数# o3 n. [2 p- g0 g3 f
net_1.trainParam.show = 50;. ]' L! d! ?5 u
net_1.trainParam.lr = 0.05;
* C0 ^% U3 ^/ c/ E& n6 ~# Snet_1.trainParam.mc = 0.9;
7 J+ I7 E! z1 P, Qnet_1.trainParam.epochs = 10000;
! T% S, t5 C4 B8 D; Tnet_1.trainParam.goal = 1e-3;6 |& {9 m( y5 T: g# ?9 J
% 调用 TRAINGDM 算法训练 BP 网络8 T8 ~: a: v6 d) ]. Z
[net_1,tr]=train(net_1,P,T);' |, I% n+ F M4 ]! a
% 对 BP 网络进行仿真2 {5 V2 P3 `+ i' g
A = sim(net_1,P);
% K W& M4 r1 G) b* u/ k% 计算仿真误差
9 z {( m. T3 _' p! M+ }E = T - A;' o1 @5 ]0 n' \9 T1 O
MSE=mse(E)2 k% s, R+ o' ]+ U! Q- H9 \
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) l5 R/ \' Q6 _
sim(net_1,x)
* Q) [$ z7 f% ~! 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 。
% R8 ?- s! j/ i5 ?4 j" c) Q. ` |
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