新版Matlab中神经网络训练函数Newff的使用方法

一、 介绍新版newff

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

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')

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

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

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)