# 新版Matlab中神经网络训练函数Newff的使用方法

Syntax

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

Examples

Here 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

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.

%% 清空环境变量

clc

clear

%% 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 on

plot(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;

end

end

rightratio=rightnumber/size(output_test,2)*100; sprintf('测试准确率=%0.2f',rightratio)