close all
clear 
echo on 
clc 
%NEWFF---生成一個新的前向神經網絡
%TRAIN---對BP神經網絡進行訓練
%SIM-----對BP神經網絡進行仿真
pause    %敲任意鍵開始
clc 

%定義訓練樣本

%P為輸入矢量 
P=[96.8476,132.8508,161.6836,212.0396,251.2583,292.3482,424.0791,993.8016,2.46E+03,1.47E+04,2.80E+04];
%T為目標矢量
T=[0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5];
pause;
clc 
%創建一個新的前向神經網絡
net=newff(minmax(P),[5,1],{'tansig','purelin'},'trainlm');
%當前輸入層權值和總閾值
inputWeights=net.IW{1,1};
inputbias=net.b{1};
%當前網絡層權值和閾值
layerWeights=net.LW{2,1};
layerbias=net.b{2};
pause 
clc 
%設置訓練參數
net.trainParam.show=50;    %
net.trainParam.lr=0.05;   %設定修正權值的學習速率  0.01~0.07
net.trainParam.mc=0.9;    %
net.trainParam.epochs=10000;  %設定循環次數
net.trainParam.goal=1e-3;    %設定期望誤差最小值
pause 
clc 
%調用TRAINGDM算法訓練網絡
[net,tr]=train(net,P,T);
pause 
clc 
%對BP網絡進行仿真
A=sim(net,P);
%計算仿真誤差
E=T-A
MSE=mse(E)
pause
clc
echo off