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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];5 C7 `1 M" h- W: Q' a
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];
: E; {* C( o$ e- q# r3 ?% `% 创建一个新的前向神经网络
) `& T* J# b/ t8 H9 Qnet_1=newff(minmax(P),[10,1],{'tansig','purelin'},'traingdm')2 z9 `" ?8 a* x$ x' ]& t8 [
% 当前输入层权值和阈值# ^8 J# P! {/ {9 L* @
inputWeights=net_1.IW{1,1}" ^) C6 N- B) X1 R, n
inputbias=net_1.b{1}; M% d" L# K1 o# U! c
% 当前网络层权值和阈值
% H7 P& w7 \) `8 blayerWeights=net_1.LW{2,1}( q4 Y0 J! ^- `
layerbias=net_1.b{2}
0 i" Q$ M& M: N& H5 s% 设置训练参数5 d! v! b$ m2 N( s4 \0 a9 n
net_1.trainParam.show = 50;3 K) F% A- n6 x1 s
net_1.trainParam.lr = 0.05;
6 o/ T& v" m2 `net_1.trainParam.mc = 0.9;
& T* U3 i1 X$ [: ? x) hnet_1.trainParam.epochs = 10000;
] R: Q* f3 t4 P8 m0 fnet_1.trainParam.goal = 1e-3;1 c M. J) x* i- X) z% ^
% 调用 TRAINGDM 算法训练 BP 网络) b4 Q! S- _1 k6 J* L6 i
[net_1,tr]=train(net_1,P,T);
1 p9 f# _% L2 N0 F/ b% 对 BP 网络进行仿真$ s) p9 m& i* I( a7 O8 J
A = sim(net_1,P);
4 m1 ]5 H" v3 z2 h5 m% 计算仿真误差 $ v8 M5 R. o& w. u Z) _8 M: t, z
E = T - A;$ P6 Y# M' B7 R- k# l
MSE=mse(E)# G% |2 k7 T/ G8 T, ]( F
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
]( T' {7 s3 h$ `sim(net_1,x)
! \ c* V, u. Y9 N" R7 G/ h1 ?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 。; A+ z. S' c+ v6 A
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