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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];
& H5 c+ D; P1 z/ LT=[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];1 D+ d3 i4 Y; j( m6 b$ C2 R: f0 u
% 创建一个新的前向神经网络
' S1 ~$ U O& h+ i' V" }$ K# v( Wnet_1=newff(minmax(P),[10,1],{'tansig','purelin'},'traingdm')
: T3 |$ Q2 e1 K4 F4 H% 当前输入层权值和阈值8 @: t9 }4 o7 b0 L G$ a" Y* o
inputWeights=net_1.IW{1,1}
# }/ ]9 P" g9 X" z' ]inputbias=net_1.b{1}! G6 B& w1 b2 W r
% 当前网络层权值和阈值' W& k' F) P( S4 P9 S& W, [$ }3 m
layerWeights=net_1.LW{2,1}: u; H* T! h3 |. t8 j6 d
layerbias=net_1.b{2}
' u2 i4 ?" b3 B5 b9 ]% G% 设置训练参数
/ G t2 L) x0 T7 k) fnet_1.trainParam.show = 50;8 y8 H' G& `, y, |7 F3 B
net_1.trainParam.lr = 0.05;
+ T1 k' J O3 K/ O. a9 @+ Rnet_1.trainParam.mc = 0.9;! K0 K: |0 J! T: w
net_1.trainParam.epochs = 10000;1 Y" _7 R. @+ r6 [# Z3 E: S. P. f/ S
net_1.trainParam.goal = 1e-3;
: n0 B5 O2 E+ R# t e% 调用 TRAINGDM 算法训练 BP 网络# J+ m* i9 c }8 N: I. K0 ~+ D
[net_1,tr]=train(net_1,P,T);
( e8 X' t p c% 对 BP 网络进行仿真
8 a6 q5 n3 p' V6 I3 Z5 fA = sim(net_1,P);: a9 L/ K* s% K) D/ }; e1 B
% 计算仿真误差 - a) Z+ v* n7 _. ]2 V
E = T - A;
5 _% P( x5 O* eMSE=mse(E)
; A7 ~0 b* }6 xx=[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: z' Q9 z1 y) H7 d4 Z& M! `% ^
sim(net_1,x) 3 ~6 X% h; i, t" O$ |) 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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zan
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