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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];, X7 X' x, w2 C8 E% O; f8 d: Y
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];6 x' x$ h* @3 Z0 c; h1 a& v
% 创建一个新的前向神经网络
v6 [- x; F7 e0 e$ m2 cnet_1=newff(minmax(P),[10,1],{'tansig','purelin'},'traingdm')
* }( Y" a2 }3 U9 \% 当前输入层权值和阈值
( p( t6 T( k% ~+ _, winputWeights=net_1.IW{1,1}
5 a/ n. T$ O% c9 w" ]8 P3 X# P, xinputbias=net_1.b{1}& F$ _& p6 Z; z( H. W
% 当前网络层权值和阈值
( W3 t5 m# G7 d) F7 l1 a; OlayerWeights=net_1.LW{2,1}; n' e, T9 |3 d) w
layerbias=net_1.b{2}
6 _/ F0 ^6 ^$ j% 设置训练参数# V3 S- V& s/ W& Q8 A; G7 o1 G
net_1.trainParam.show = 50;
# g6 h5 d7 u+ [, R, n# H/ ^net_1.trainParam.lr = 0.05;8 r3 N, \9 n3 m X
net_1.trainParam.mc = 0.9;: D6 u4 E4 n4 |3 F; q
net_1.trainParam.epochs = 10000;6 M, v9 q3 y6 Y$ i2 z# ^
net_1.trainParam.goal = 1e-3;( ^3 n' ~- ~% U0 u; Y+ b
% 调用 TRAINGDM 算法训练 BP 网络1 ]' _+ a" L0 |" K" w* u
[net_1,tr]=train(net_1,P,T);
# C( F( u9 |. i0 I8 M% 对 BP 网络进行仿真
7 j) i& ?8 F2 _" WA = sim(net_1,P);4 m* A2 d' e/ C3 t' i2 x# d2 Z
% 计算仿真误差 . Q# P( S( b. R$ N ~. f Y& N8 R
E = T - A;6 F, N3 v* @# W1 L5 w) f
MSE=mse(E)7 R" c% v* t; H
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% _7 s4 J3 ]) S
sim(net_1,x)
7 K) s2 ^1 t$ f* m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; [* p% F' f: [
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