function?error?=?fun（xinputnumhiddennumoutputnumnetinputnoutputn）
%該函數(shù)用來計算適應(yīng)度值
%x??????????input?????個體
%inputnum???input?????輸入層節(jié)點(diǎn)數(shù)
%outputnum??input?????隱含層節(jié)點(diǎn)數(shù)
%net????????input?????網(wǎng)絡(luò)
%inputn?????input?????訓(xùn)練輸入數(shù)據(jù)
%outputn????input?????訓(xùn)練輸出數(shù)據(jù)

%error??????output????個體適應(yīng)度值

%提取?BP神經(jīng)網(wǎng)絡(luò)初始權(quán)值和閾值，x為個體
w1=x（1:inputnum*hiddennum）;
B1=x（inputnum*hiddennum+1:inputnum*hiddennum+hiddennum）;
w2=x（inputnum*hiddennum+hiddennum+1:inputnum*hiddennum+hiddennum+hiddennum*outputnum）;
B2=x（inputnum*hiddennum+hiddennum+hiddennum*outputnum+1:inputnum*hiddennum+hiddennum+hiddennum*outputnum+outputnum）;

?
%網(wǎng)絡(luò)權(quán)值賦值
net.iw{11}=reshape（w1hiddennuminputnum）;
net.lw{21}=reshape（w2outputnumhiddennum）;
net.b{1}=reshape（B1hiddennum1）;
net.b{2}=B2;
%BP神經(jīng)網(wǎng)絡(luò)構(gòu)建
net=newff（inputnoutputnhiddennum）;
net.trainParam.epochs=100;
net.trainParam.lr=0.1;
net.trainParam.goal=0.00001;
net.trainParam.show=100;
net.trainParam.showWindow=0;

%BP神經(jīng)網(wǎng)絡(luò)訓(xùn)練
net=train（netinputnoutputn）;

%網(wǎng)絡(luò)訓(xùn)練
an=sim（netinputn）;

error=sum（abs（an-outputn））;

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????目錄???????????0??2015-06-17?16:49??新建文件夾\
?????文件???????89946??2015-06-16?14:53??新建文件夾\data.mat
?????文件????????1139??2015-06-09?09:54??新建文件夾\fun.m
?????文件??????469909??2015-06-16?10:26??新建文件夾\input_test.mat
?????文件??????469909??2015-06-16?10:26??新建文件夾\input_train.mat
?????文件??????469909??2015-06-16?10:26??新建文件夾\output_test.mat
?????文件??????469909??2015-06-16?10:26??新建文件夾\output_train.mat
?????文件????????3284??2015-06-17?16:39??新建文件夾\PSO.m