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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];
8 h- o7 U) i- a$ R. l/ X1 A 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];
( o; d F; ?# I* |, X% 创建一个新的前向神经网络 ( m6 [+ }, J4 k) P) H
net_1=newff(minmax(P),[10,1],{'tansig','purelin'},'traingdm')3 \+ d, @& H6 w' Z2 w1 ]9 l
% 当前输入层权值和阈值2 L# h3 L5 q+ o9 S
inputWeights=net_1.IW{1,1}
4 `" x" G# H, t5 h5 _inputbias=net_1.b{1}
* d. l# b- B* S5 l$ N6 J% 当前网络层权值和阈值
+ u, Y" q0 H" e# |layerWeights=net_1.LW{2,1}- H8 e# u- M5 a- w: h/ U
layerbias=net_1.b{2}
9 R! Z8 k9 q3 e& L! I% 设置训练参数1 d# F6 ^& C, z9 r1 z0 x5 Q
net_1.trainParam.show = 50;
( g9 [/ j) N+ k; K. ^7 h Knet_1.trainParam.lr = 0.05;9 ?' j' U. F/ E
net_1.trainParam.mc = 0.9;( f% X2 v8 N! H# J V
net_1.trainParam.epochs = 10000;
, b) u/ X) K/ ~; { Rnet_1.trainParam.goal = 1e-3;. I* ]& z& x) F% U5 `, y6 }
% 调用 TRAINGDM 算法训练 BP 网络
: C" d6 p( }# f; z8 g[net_1,tr]=train(net_1,P,T);, F( L& @0 G, }
% 对 BP 网络进行仿真' i4 S ~9 t- q* Y
A = sim(net_1,P);8 E) @6 P6 T0 R0 a$ e& v" u
% 计算仿真误差
& a0 z/ n2 X5 H8 B( |" f- DE = T - A;
& k3 S3 V' t( T: jMSE=mse(E)1 k" O" z9 ?9 I% d
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) p/ s2 D5 O$ o6 g; D3 Z
sim(net_1,x)
: |8 t' b1 Q8 I* b% w; A- o4 w5 D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 。
: G( P6 {" Q9 V* y |
zan
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