clearclc;
x1=xlsread（‘G:\研究生論文寫作及實驗\DBC-14-05插值.xlsx‘‘A1:A102‘）;??
A2=xlsread（‘G:\研究生論文寫作及實驗\DBC-14-05插值.xlsx‘‘A103:A126‘）‘;
x=x1‘;
%?該腳本用來做NAR神經網絡預測??
lag=10;????%?自回歸階數??
iinput=x;????%?x為原始序列（行向量）??
n=length（iinput）;??
??
%準備輸入和輸出數據??
inputs=zeros（lagn-lag）;??
for?i=1:n-lag??
????inputs（:i）=iinput（i:i+lag-1）‘;??
end??
targets=x（lag+1:end）;??
??
%創建網絡??
%?hiddenlayerSize?=10;?%隱藏層神經元個數??
%?net?=?fitnet（hiddenlayerSize）;??
%?net.trainParam.epochs=10;
%?net.trainParam.goal=0.00000001;
%?net.trainFcn=‘trainbr‘;

net=newff（inputstargets10{‘tansig‘‘tansig‘‘dtansig‘}‘trainlm‘‘learngdm‘‘msereg‘）;
net=init（net）;
net.performFcn=‘sse‘;
net.trainParam.show=20;
%?net.trainParam.Lr=0.05;
net.trainParam.mc=0.92;
net.trainParam.epochs=50;
net.trainParam.goal=0.01;


??
%?%?避免過擬合，劃分訓練，測試和驗證數據的比例??
net.divideParam.trainRatio?=?70/100;??
net.divideParam