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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];/ |% i, h& C9 |9 p
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];
# F* C3 }2 c2 g% 创建一个新的前向神经网络 : J7 Q; I [; i- Y
net_1=newff(minmax(P),[10,1],{'tansig','purelin'},'traingdm')/ b! i9 _! n/ w# Q8 T4 b$ t
% 当前输入层权值和阈值. d1 x+ f. m8 P7 \" E
inputWeights=net_1.IW{1,1}
# r1 }( C4 i" q) u! sinputbias=net_1.b{1}+ C+ m/ I7 Z2 }$ N7 {7 \
% 当前网络层权值和阈值8 V, B6 Q7 B2 r3 w
layerWeights=net_1.LW{2,1}! R+ u5 R' o8 N9 C% Q1 P! f
layerbias=net_1.b{2}
) G" l) M4 W; m% 设置训练参数
( c- l% i& Y G" k$ D* enet_1.trainParam.show = 50;
$ l; D- M2 H2 a+ ynet_1.trainParam.lr = 0.05;
% `- o- C" V* o2 @% b0 s% N1 Vnet_1.trainParam.mc = 0.9;/ k: o$ ^* A7 K9 R, v
net_1.trainParam.epochs = 10000;$ \9 V4 ] r/ |6 t" B
net_1.trainParam.goal = 1e-3;
! d# t( b0 |' c; n4 R) o% 调用 TRAINGDM 算法训练 BP 网络 P, k2 ]) d d1 K
[net_1,tr]=train(net_1,P,T);
\/ I/ Q/ m% ]- d% 对 BP 网络进行仿真) q+ w9 b% c7 n- q# d) ~+ q; ?
A = sim(net_1,P);9 z) r; o$ v. @
% 计算仿真误差
: d' r4 F5 R) M" iE = T - A;' ]6 X5 {3 x" v! e0 D7 R0 C
MSE=mse(E)* I4 U( v2 p6 H6 D: z0 X6 _5 H8 r4 e
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' v/ L, j0 J8 @3 ~. X% X1 J4 L
sim(net_1,x)
, d' C' d' o3 R+ c. P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 。) l4 h/ Q# [! g g1 A5 o# B
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