BP神经网络matlab实例简单而经典

1、p=p1't=t1'pn,minp,maxp,tn,mint,maxt=premnmx(p,t);%原始数据归一化net=newff(minmax(pn),5,1,'tansig','purelin','traingdx');%设置网络，建立相应的BP网络net.trainParam.show=2000;%训练网络net.trainParam.lr=0.01;net.trainParam.epochs=100000;net.trainParam.goal=1e-5;net,tr=train(net,pn,tn);网络%调用TRAI

2、NGDM算法训练BPpnew=pnew1'pnewn=tramnmx(pnew,minp,maxp);anewn=sim(net,pnewn);anew=postmnmx(anewn,mint,maxt);y=anew'%对BP网络进行仿真%还原数据1、BP网络构建(1)生成BP网络netnewff(PR,S1S2.SNl,TF1TF2.TFNI,BTF,BLF,PF)PR：由R维的输入样本最小最大值构成的R2维矩阵。S1S2.SNI：各层的神经元个数。TF1TF2.TFNI：各层的神经元传递函数。BTF：训练用函数的名称。(2)网络训练net,tr,Y,E,Pf,Aftrai

3、n(net,P,T,Pi,Ai,VV,TV)(3)网络仿真Y,Pf,Af,E,perfsim(net,P,Pi,Ai,T)训练方法训练函数梯度卜降法traingd有动量的梯度卜.降法traingdm自适应lr梯度卜.降法traingda自适应lr动量梯度卜.降法traingdx弹性梯度卜.降法trainrpFletcher-Reeves共轲梯度法traincgfPloak-Ribiere共轲梯度法traincgp'tansig','purelin','trainrp'BP网络的训练函数Powell-Beale共轲梯度法traincgb量化共辗梯度

4、法trainscg拟牛顿算法trainbfg一步正割算法trainossLevenberg-MarquardttrainlmBP网络训练参数训练参数参数介绍训练函数net.trainParam.epochs最大训练次数（缺省为10）traingd、traingdm、traingda、traingdx、trainrp、traincgf、traincgp>traincgb、trainscg、trainbfg、trainoss、trainlmnet.trainParam.goal训练要求精度（缺省为0）traingd、traingdm、traingda、traingdx、trainrp>

5、traincgf、traincgp、traincgb、trainscg、trainbfg、trainoss、trainlmnet.trainParam.lr学习率（缺省为0.01）traingd、traingdm、traingda、traingdx、trainrp、traincgf、traincgp、traincgb、trainscg、trainbfg、trainoss、trainlmnet.trainParam.max_fail最大失败次数（缺省为5）traingd、traingdm、traingda、traingdx、trainrp、traincgf、traincgp、traincgb、t

6、rainscg、trainbfg、trainoss、trainlmnet.trainParam.min_grad最小梯度要求（缺省为1e-10）traingd、traingdm、traingda、traingdx、trainrp、traincgf、traincgp、traincgb、trainscg、trainbfg、trainoss、trainlmnet.trainParam.show显示训练迭代过程（NaN表示不显示，缺省为25）traingd、traingdm、traingda、traingdx、trainrp、traincgf、traincgp、traincgb、trainscg、tr

7、ainbfg、trainoss、trainlmnet.trainParam.time最大训练时间（缺省为inf）traingd、traingdm、traingda、traingdx、trainrp、traincgf、traincgp、traincgb、trainscg、trainbfg、trainoss、trainlmnet.trainParam.mc动量因子（缺省0.9）traingdm、traingdxnet.trainParam.lr_inc学习率lr增长比（缺省为1.05）traingda、traingdxnet.trainParam.lr_dec学习率lr下降比（缺省为0.7）tra

8、ingda、traingdxnet.trainParam.max_perf_inc表现函数增加最大比（缺省为1.04）traingda、traingdxnet.trainParam.delt_inc权值变化增加量（缺省为1.2）trainrpnet.trainParam.delt_dec权值变化减小量（缺省为0.5）trainrpnet.trainParam.delt0初始权值变化（缺省为0.07）trainrpnet.trainParam.deltamax权值变化最大值（缺省为50.0）trainrpnet.trainParam.searchFcn一维线性搜索方法（缺省为srchcha）tr

9、aincgf>traincgp>traincgb、trainbfg、trainossnet.trainParam.sigma因为二次求导对权值调整的影响参数（缺省值5.0e-5）trainscgnet.trainParam.lambdaHessian矩阵小确定性调节参数（缺省为5.0e-7）trainscgnet.trainParam.men_reduc控制计算机内存/速度的参量，内存较大设为1,否则设为2（缺省为1）trainlmnet.trainParam.mu的初始值（缺省为0.001）trainlmnet.trainParam.mu_dec的减小率（缺省为0.1）train

10、lmnet.trainParam.mu_inc的增长率（缺省为10）trainlmnet.trainParam.mu_max的最大值（缺省为1e10）trainlm2、BP网络举例举例1、%traingdclear;clc;P=-1-1224;05057;T=-1-111-1;%用minmax函数求输入样本范围net=newff(minmax(P),T,5,1,'tansig','purelin','trainrp');net.trainParam.show=50;%net.trainParam.lr=0.05;net.trainParam.ep

11、ochs=300;net.trainParam.goal=1e-5;net,tr=train(net,P,T);net.iw1,1%隐层权值net.b1%隐层阈值net.lw2,1%输出层权值net.b2%输出层阈值sim(net,P)举例2、利用三层BP神经网络来完成非线性函数的逼近任务，其中隐层神经元个数为五个。样本数据：输入X输出D输入X输出D输入X输出D-1.0000-0.9602-0.30000.13360.40000.3072-0.9000-0.5770-0.2000-0.20130.50000.3960-0.8000-0.0729-0.1000-0.43440.60000.344

12、9-0.70000.37710-0.50000.70000.1816-0.60000.64050.1000-0.39300.8000-0.3120-0.50000.66000.2000-0.16470.9000-0.2189-0.40000.46090.3000-0.09881.0000-0.3201解:看到期望输出的范围是1,1,所以利用双极性Sigmoid函数作为转移函数。程序如下：clear;clc;X=-1:0,1:1;D=-0.9602-0.5770-0.07290.37710.64050.66000.46090.1336-0.2013-0.4344-0.5000-0.3930-0.

13、1647-.09880.30720.39600.34490.1816-0.312-0.2189-0.3201;figure;plot(X,D,figure;plot(X,D,）;%绘制原始数据分布图（附录:net=newff(-11,51,'tansig'1-1),'tansig');net.trainParam.epochs=1000;net.trainParam.goal=0.005;net=train(net,X,D);O=sim(net,X);figure;net.trainParam.epochs=1000;net.trainParam.goal=0.

14、005;net=train(net,X,D);O=sim(net,X);figure;%训练的最大次数%全局最小误差plot(X,D,plot(X,D,X,O);V=net.iw1,1;theta1=net.b1;W=net.lw2,1;theta2=net.b2;所得结果如下:%绘制训练后得到的结果和误差曲线（附录:%输入层到中间层权值%中间层各神经元阈值%中间层到输出层权值%输出层各神经元阈值1-2、1-3)输入层到中间层的权值:-9.16697.34487.37614.89663.5406中间层各神经元的阈值:6.5885-2.4019-0.99621.53033.273T中间层到输出层

15、的权值:0.34270.21350.2981-0.88401.9134输出层各神经元的阈值:-1.5271举例3、利用三层BP神经网络来完成非线性函数的逼近任务，其中隐层神经元个数为五个。样本数据：输入X输出D输入X输出D输入X输出D00448211539322621043371解:看到期望输出的范围超出1,1,所以输出层神经元利用线性函数作为转移函数。程序如下:clear;clc;X=012345678910;D=01234321234;figure;plot(X,D,'*');%绘制原始数据分布图net=newff(010,51,'tansig','

16、purelin')net.trainParam.epochs=100;net.trainParam.goal=0.005;net=train(net,X,D);O=sim(net,X);figure;plot(X,D,'*',X,O);%绘制训练后得到的结果和误差曲线(附录：2-2、2-3)V=net.iw1,1theta1=net.b1W=net.lw2,1theta2=net.b2所得结果如下：%输入层到中间层权值%用间层各神经元阈值%中间层到输出层权值%输出层各神经元阈值输入层到中间层的权值:V0.85842.0890-1.21660.2752-0.391CT中间

17、层各神经元的阈值:T-14.0302-9.83407.4331-2.01350.5610中间层到输出层的权值:W-0.4675-1.12342.32084.6402-2.2686输出层各神经元的阈值：T1.7623问题：以下是上证指数2009年2月2日到3月27日的收盘价格，构建一个三层BP神经网络，利用该组信号的6个过去值预测信号的将来值。日期价格日期价格2009/02/022011.6822009/03/022093.4522009/02/032060.8122009/03/032071.4322009/02/042107.7512009/03/042198.1122009/02/0520

18、98.0212009/03/052221.0822009/02/062181.2412009/03/062193.0122009/02/092224.7112009/03/092118.7522009/02/102265.1612009/03/102158.5722009/02/112260.8222009/03/112139.0212009/02/122248.0922009/03/122133.8812009/02/132320.7922009/03/132128.8512009/02/162389.3922009/03/162153.2912009/02/172319.4422009/0

19、3/172218.3312009/02/182209.8622009/03/182223.7312009/02/192227.1322009/03/192265.7612009/02/202261.4822009/03/202281.0912009/02/232305.7822009/03/232325.4812009/02/242200.6522009/03/242338.4212009/02/252206.5722009/03/252291.5512009/02/262121.2522009/03/262361.7012009/02/272082.8522009/03/272374.44loaddata3_1.txt;m,n=size(data3_1);tsx=data3_1(1:m-1,1);tsx=tsx'ts=data3_1(2:m,1);ts=ts'TSX,TSXps=mapminmax(tsx,1,2);TS,TSps=mapminmax(ts,1,2);TSX=TSX'figure;plot(ts,'LineWidth&