matlab BP神经网络训练程序求解释

楼主解决没？这是我知道的

[pn,minp,maxp,tn,mint,maxt]=premnmx(p,t)%归一化数据，方便后面的预测

net.trainParam. show = 100 %这里培弊的show是显示步数配败族，每100步显示一次

net.trainParam.goal=0.0001%目标误差，训练得到的数据和原始输入

net.trainParam.lr = 0.01 %lr是学习动量，一般越小越好

y1=sim(net,pn) %sim用来预测的

xlswrite('testdata6',tnew1) ？这里的testdata6是excel表枯核格的名称

你可以看看书的，书上都有介绍

P=[。。。]输入T=[。。。]输出

% 创建一个新的前向神经网络

net_1=newff(minmax(P),[10,1],{'tansig','purelin'},'traingdm')

% 当前输入层权值和阈睁培值

inputWeights=net_1.IW{1,1}

inputbias=net_1.b{1}

% 当前网络层权值和阈值

layerWeights=net_1.LW{2,1}

layerbias=net_1.b{2}

% 设置训练参春碰数

net_1.trainParam.show = 50

net_1.trainParam.lr = 0.05

net_1.trainParam.mc = 0.9

net_1.trainParam.epochs = 10000

net_1.trainParam.goal = 1e-3

% 调用 TRAINGDM 算法训练 BP 网络悉森唯

[net_1,tr]=train(net_1,P,T)

% 对 BP 网络进行仿真

A = sim(net_1,P)

% 计算仿真误差

E = T - A

MSE=mse(E)

x=[。。。]'%测试

sim(net_1,x)

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

不可能啊 我2009

x=[0.45 420.32 420.47 510.52 500.88 60.92 30.01 210.06 40.58 480.78 44]

y=[1011000011]

inputs = x'

targets = y'

hiddenLayerSize = 8

net = patternnet(hiddenLayerSize)

net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'}

net.outputs{2}.processFcns = {'removeconstantrows','桥差森mapminmax'}

net.divideFcn = 'dividerand' % Divide data randomly

net.divideMode = 'sample'敏亩 % Divide up every sample

net.divideParam.trainRatio = 70/100

net.divideParam.valRatio = 15/100

net.divideParam.testRatio = 15/100

net.trainFcn = 'trainlm' % Levenberg-Marquardt

net.performFcn = 'mse' % Mean squared error

net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...

'plotregression', 'plotfit'}

[net,tr] = train(net,inputs,targets)

outputs = net(inputs)

errors = gsubtract(targets,outputs)

performance = perform(net,targets,outputs)

trainTargets = targets .* tr.trainMask{1}

valTargets = targets .* tr.valMask{1}

testTargets = targets .* tr.testMask{1}

trainPerformance = perform(net,trainTargets,outputs)

valPerformance = perform(net,valTargets,outputs)

testPerformance = perform(net,testTargets,outputs)

view(net)

训练的庆散模型保存在net这个结构体中，想通过输入得到输出用sim（）函数

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