%適應度函數
function?y=AdaptFunc_BP（x）
%求解偏差的均方根，也就是每個預測點和實際點的平方和再求平方根
%x是行向量，數量為ParticleSize
%x（1:5）---V:輸入到隱層神經元的權值
%x（6:10）---theta1:隱層神經元的閾值
%x（11:15）---W:輸出層神經元的連接權
%x（16）---W:輸出層神經元的閾值

[rowcol]=size（x）;

%訓練集的X
X=-1:0.1:1;
%col2有用
[row2col2]=size（X）;

D=[-0.9602?-0.5770?-0.0729?0.3771?0.6405?0.6600?0.4609?...
????0.1336?-0.2013?-0.4344?-0.5000?-0.3930?-0.1647?-.0988?...
????0.3072?0.3960?0.3449?0.1816?-0.312?-0.2189?-0.3201];

if?row>1
????error（‘適應度函數：輸入的參數錯誤‘）;
end

%net?=?newff（[-1?1][5?1]{‘tansig‘‘tansig‘}）;
net?=?newff（XD5{‘tansig‘‘tansig‘}）;
%net.trainParam.epochs?=?100;?%訓練的最大次數
%net.trainParam.goal?=?0.005;?%全局最小誤差
?V=x（1:5）‘;
?theta1=x（6:10）‘;
?W=x（11:15）;
?theta2=x（16）;
?net.iw{11}=V;
?net.b{1}=theta1;
?net.lw{21}=W;
?net.b{2}=theta2;
?O2?=?sim（netX）;
?
?%計算當前參數下，神經網絡的實際輸出O2與目標輸出D之間的誤差的平方和
?total=sqrt（sum（（O2-D）.^2））;
????TempStr=sprintf（‘誤差平方和%g‘total）;
????disp（TempStr）;
y=total;

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?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----

?????文件???????1205??2014-06-18?20:36??AdaptFunc_BP.m

?????文件????????572??2014-06-19?09:25??Check_PSO_Result_BP.m

?????文件???????1011??2014-06-18?20:31??ContrastBP_PSO.m

?????文件??????15572??2014-06-19?09:58??PSO_Stand.m

?????文件????????669??2014-06-19?10:44??ShowAllResult.asv

?????文件????????693??2014-06-19?10:49??ShowAllResult.m

?????文件???????1594??2014-06-19?11:14??Test_PSO_Stand.m

?????文件?????611072??2014-06-19?11:21??采用粒子群進行曲線擬合的過程.docx

?????文件????????594??2014-06-19?12:12??使用說明.txt

-----------?---------??----------?-----??----

???????????????632982????????????????????9