close all 
                  clear 
                  echo on 
                  clc 
                  % NEWFF——生成一個(gè)新的前向神經(jīng)網(wǎng)絡(luò) 
                  % TRAIN——對(duì) BP 神經(jīng)網(wǎng)絡(luò)進(jìn)行訓(xùn)練 
                  % SIM——對(duì) BP 神經(jīng)網(wǎng)絡(luò)進(jìn)行仿真 
                  pause        
                  %  敲任意鍵開(kāi)始 
                  clc 
                  %  定義訓(xùn)練樣本 
                  % P 為輸入矢量 
                  P=[-1,  -2,    3,    1;       -1,    1,    5,  -3];
                  % T 為目標(biāo)矢量 
                  T=[-1, -1, 1, 1]; 
                  pause; 
                  clc 
                  %  創(chuàng)建一個(gè)新的前向神經(jīng)網(wǎng)絡(luò) 
                  net=newff(minmax(P),[3,1],{'tansig','purelin'},'traingdm')
                  %  當(dāng)前輸入層權(quán)值和閾值 
                  inputWeights=net.IW{1,1} 
                  inputbias=net.b{1} 
                  %  當(dāng)前網(wǎng)絡(luò)層權(quán)值和閾值 
                  layerWeights=net.LW{2,1} 
                  
                  
                  layerbias=net.b{2} 
                  pause 
                  clc 
                  %  設(shè)置訓(xùn)練參數(shù) 
                  net.trainParam.show = 50; 
                  net.trainParam.lr = 0.05; 
                  net.trainParam.mc = 0.9; 
                  net.trainParam.epochs = 1000; 
                  net.trainParam.goal = 1e-3; 
                  pause 
                  clc 
                  %  調(diào)用 TRAINGDM 算法訓(xùn)練 BP 網(wǎng)絡(luò) 
                  [net,tr]=train(net,P,T); 
                  pause 
                  clc 
                  %  對(duì) BP 網(wǎng)絡(luò)進(jìn)行仿真 
                  A = sim(net,P) 
                  %  計(jì)算仿真誤差 
                  E = T - A 
                  MSE=mse(E) 
                  pause 
                  clc
                  x=[1;2;3;4];
                  figure;
                  plot(x,T,'*r',x,A,'ob')
                  axis([0 5 -1.5 1.5]);
                  clc 
                  echo off