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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];/ \$ h' ~0 c- N. a% J9 _. D
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];' X; @& I8 t; @( M
% 创建一个新的前向神经网络 % p* I8 P4 L/ Y% }- R* A8 ^
net_1=newff(minmax(P),[10,1],{'tansig','purelin'},'traingdm')
9 J& A7 m& e# ?& S% 当前输入层权值和阈值
& _+ _) O# F2 q7 v, s9 j3 l% N3 binputWeights=net_1.IW{1,1}) }; _7 l: e3 `! Z0 c5 Q
inputbias=net_1.b{1}0 y" [4 b" z# H# b! {. |+ ~
% 当前网络层权值和阈值 [8 f. D4 r# ?) W- l0 o2 X
layerWeights=net_1.LW{2,1}: N: x! K' Q4 f8 V8 O; m
layerbias=net_1.b{2}2 U) J! l! F8 b2 P# q1 a
% 设置训练参数7 L- n" s- q! x2 i, c! [
net_1.trainParam.show = 50;! l, u* l. ~4 o( k, B3 \
net_1.trainParam.lr = 0.05;6 k8 ?, ~- o% e! L/ ?" x& ^0 u8 j% A
net_1.trainParam.mc = 0.9;- x9 _+ l: @; v3 U
net_1.trainParam.epochs = 10000;8 ?' [1 F& x2 G' a
net_1.trainParam.goal = 1e-3;& R, W, ~ M/ `: h
% 调用 TRAINGDM 算法训练 BP 网络
/ w* J4 E+ I1 ]' D[net_1,tr]=train(net_1,P,T);3 c+ {. Y! N1 F" o
% 对 BP 网络进行仿真
& w' b a& }. C2 s) I5 ~A = sim(net_1,P);" n& ^9 Z( o: }2 t+ q7 V
% 计算仿真误差 + b( H7 F. A r, b4 n4 c, I
E = T - A;; M1 Y8 _" ?) f' ]; Z* L" V
MSE=mse(E)
3 S3 m: n9 f: r; a8 a& j* qx=[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
% G' Z3 w: N2 V' [* X D/ L! Fsim(net_1,x) + U K2 Z4 n9 j$ [
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 。7 S( X$ E$ |3 @: k. L$ T; B2 r, [
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