clc;clear;close?all
disp（‘PLS預測的RMSECVRMSEP訓練集及預測集的R‘）
load?TenSesameAddSoybeanOil_NIR_20150121
meanornot?=?1;
mean_method?=?1;
switch?mean_method
????case?1
???????for?i?=?1:50
????????Spectra（i:）=?mean（spectra（3*i-2:3*i:））;?????????????
???????end
???????Target?=?target;
????case?2
???????Spectra?=?spectra;
???????Target（（3*i-2）:（3*i））??=?target（i）;?
end
for?columm??=?1;
????name???=?ynames;
disp（name{columm}）;
%-----------------KS對數據劃分，訓練集2/3，預測集1/3-------------------------
[mn]????????????????????=?size（cal）;
[modeltest]?????????????=?kenstone（calfloor（2/3*m））;
x_train??????????????????=?cal（model:）;
x_pred???????????????????=?cal（test:）;
y_train??????????????????=?caltar（modelcolumm）;
y_pred???????????????????=?caltar（testcolumm）;
[m_trainn_train]????????=?size（x_train）;
%———————————----——數據中心化—————————--------------------
pretreatment?=?3;%對于ANN，采用第2個中心化最好
switch?pretreatment;
???case?1?%中心化
???mean_x_train?????????=?mean（x_train）;?
???x_train??????????????=?x_train?-?repmat（mean_x_trainsize（x_train1）1）;
???x_pred???????????????=?x_pred?-?repmat（mean_x_trainsize（x_pred1）1）;
???mean_y_train?????????=?mean（y_train）;
???y_train??????????????=?y_train?-?repmat（mean_y_trainsize（x_train1）1）;?
???case?2?%Normalize
???std_x_train??????????=?std（x_train01）;
???mean_x_train?????????=?mean（x_train1）;
???std_y_train??????????=?std（y_train01）;
???mean_y_train?????????=?mean（y_train）;???
???y_train??????????????=?（y_train?-?repmat（mean_y_trainsize（y_train1）1））./repmat（std_y_trainsize（y_train1）1）;?
???x_train??????????????=?（x_train-repmat（mean_x_trainsize（x_train1）1））./repmat（std_x_trainsize（x_train1）1）;
???x_pred???????????????=?（x_pred-repmat（mean_x_trainsize（x_pred1）1））./repmat（std_x_trainsize（x_pred1）1）;
???case?3??%最大最小歸一化?
???[train_xps_x]???????=?mapminmax（x_train‘）;
???[train_yps_y]???????=?mapminmax（y_train‘）;
???x_train??????????????=?train_x‘;
???y_train??????????????=?train_y‘;
???pred_x???????????????=?mapminmax（‘apply‘x_pred‘ps_x）;
???x_pred???????????????=?pred_x‘;
???y_pred???????????????=?mapminmax（‘apply‘y_predps_y）;???
end
%-------------------------BP-ANN外部預測------------------------------------
disp（‘BP-ANN直接預測‘）;??%學習速率，動量項，隱含層節點數為nodenumber
net????????????????????=?newff（minmax（x_train‘）[nodenumber1]{‘tansig‘‘purelin‘}‘traingdm‘）;
%訓練函數trainlm使用的學習算法為Levenberg-Marquadt，優點收斂速度快
%訓練函數traingd使用的學習算法是梯度下降法，收斂速度很慢
%動量BP算法:訓練函數traingdm（Gradient?descent?with?momentum?backpropagation）
%彈性BP算法：訓練函數traingrp（）
%準牛頓BP算法：訓練函數trainbfg（）
inputWeights???????????=?net.IW{11};
inputbias??????????????=?net.b{1};
layerWeights???????????=?net.LW{21};?
layerbias??????????????=?net.b{2};????
%net.trainParam.show????=?50;???%訓練顯示間隔
net.trainParam.lr??????=?0.01;?%學習速率Learning?rate，默認值0.01
%net.trainParam.mc??????=?0.9;??%動量項Momentum?constant，默認值0.9
net.trainParam.epochs??=?2000;??%最大訓練次數??
net.trainParam.goal????=?1e-3;??
[nettr]???