新版Matlab中神经网络训练函数Newff的使用方法

新版Matlab中神经网络训练函数Newff的使用方法一、   介绍新版newffSyntax·         net = newff(P,T,[S1 S2...S(N-l)],{TF1 TF2...TFNl}, BTF,BLF,PF,IPF,OPF,DDF)Description newff(P,T,[S1 S2...S(N-l)],{TF1 TF2...TFNl}, BTF,BLF,PF,IPF,OPF,DDF) takes several arguments

P	R x Q1 matrix of Q1 sample R-element input vectors
T	SN x Q2 matrix of Q2 sample SN-element target vectors
Si	Size of ith layer, for N-1 layers, default = [ ]. (Output layer size SN is determined from T.)
TFi	Transfer function of ith layer. (Default = 'tansig' for hidden layers and 'purelin' for output layer.)
BTF	Backpropagation network training function (default = 'trainlm')
BLF	Backpropagation weight/bias learning function (default = 'learngdm')
IPF	Row cell array of input processing functions. (Default = {'fixunknowns','removeconstantrows','mapminmax'})
OPF	Row cell array of output processing functions. (Default = {'removeconstantrows','mapminmax'})
DDF	Data divison function (default = 'dividerand')

 ExamplesHere is a problem consisting of inputs P and targets T to be solved with a network.·         P = [0 1 2 3 4 5 6 7 8 9 10];T = [0 1 2 3 4 3 2 1 2 3 4];Here a network is created with one hidden layer of five neurons.·         net = newff(P,T,5);The network is simulated and its output plotted against the targets.·         Y = sim(net,P);plot(P,T,P,Y,'o')The network is trained for 50 epochs. Again the network's output is plotted.·         net.trainParam.epochs = 50;net = train(net,P,T);Y = sim(net,P);plot(P,T,P,Y,'o')二、   新版newff与旧版newff调用语法对比Example1比如输入input（6*1000），输出output为（4*1000），那么 旧版定义：net=newff(minmax(input),[14,4],{'tansig','purelin'},'trainlm');新版定义：net=newff(input,output,14,{'tansig','purelin'},'trainlm');Example2比如输入input（6*1000），输出output为（4*1000），那么 旧版定义：net=newff(minmax(input),[49,14,4],{'tansig','tansig','tansig'},'traingdx');新版定义：net=newff(input,output, [49,14], {'tansig','tansig','tansig'},'traingdx');  三、   旧版newff使用方法在新版本中使用提示：旧版本定义的newff虽也能在新版本中使用，但会有警告，警告如下： Warning: NEWFF used in an obsolete way.
> In obs_use at 18
  In newff>create_network at 127
  In newff at 102
          See help for NEWFF to update calls to the new argument list.四、   新版newff与旧版newff使用的训练效果对比     旧版本：旧用法训练次数多，但精度高  新版本：新用法训练次数少，但精度可能达不到要求造成上述原因是：程序里面的权值、阈值的初始值是随机赋值的，所以每次运行的结果都会不一样，有好有坏。
你可以把预测效果不错的网络的权值和阈值作为初始值。
具体可以查看net.iw{1,1}、net.lw{2,1}、net.b{1}、net.b{2}的值。现在给一个完整的例子 %% 清空环境变量clcclear%% 训练数据预测数据data=importdata('test.txt');%从1到768间随机排序k=rand(1,768);[m,n]=sort(k); %输入输出数据input=data(:,1:8);output =data(:,9); %随机提取500个样本为训练样本，268个样本为预测样本input_train=input(n(1:500),:)';output_train=output(n(1:500),:)';input_test=input(n(501:768),:)';output_test=output(n(501:768),:)'; %输入数据归一化[inputn,inputps]=mapminmax(input_train); %% BP网络训练% %初始化网络结构net=newff(inputn,output_train,10); net.trainParam.epochs=1000;net.trainParam.lr=0.1;net.trainParam.goal=0.0000004;%% 网络训练net=train(net,inputn,output_train); %% BP网络预测%预测数据归一化inputn_test=mapminmax('apply',input_test,inputps); %网络预测输出BPoutput=sim(net,inputn_test); %% 结果分析%根据网络输出找出数据属于哪类BPoutput(find(BPoutput<0.5))=0;BPoutput(find(BPoutput>=0.5))=1; %% 结果分析%画出预测种类和实际种类的分类图figure(1)plot(BPoutput,'og')hold onplot(output_test,'r*');legend('预测类别','输出类别')title('BP网络预测分类与实际类别比对','fontsize',12)ylabel('类别标签','fontsize',12)xlabel('样本数目','fontsize',12)ylim([-0.5 1.5]) %预测正确率rightnumber=0;for i=1:size(output_test,2)    if BPoutput(i)==output_test(i)        rightnumber=rightnumber+1;    endendrightratio=rightnumber/size(output_test,2)*100; sprintf('测试准确率=%0.2f',rightratio)