(3) Use TRAINRP which is slower but more memory efficient than TRAINBFG.
参数说明
The learning function BLF can be either of the backpropagation learning functions such as LEARNGD, or LEARNGDM.
P - NixTS cell array, each element P{i,ts} is an RixQ matrix. T - NtxTS cell array, each element P{i,ts} is an VixQ matrix. Pi - NixID cell array, each element Pi{i,k} is an RixQ matrix. Ai - NlxLD cell array, each element Ai{i,k} is an SixQ matrix. Y - NOxTS cell array, each element Y{i,ts} is an UixQ matrix. E - NtxTS cell array, each element P{i,ts} is an VixQ matrix. Pf - NixID cell array, each element Pf{i,k} is an RixQ matrix. Af - NlxLD cell array, each element Af{i,k} is an SixQ matrix.
TRAIN(NET,P,T,Pi,Ai) takes,
NET - Network. P - Network inputs. T - Network targets, default = zeros. Pi - Initial input delay conditions, default = zeros. Ai - Initial layer delay conditions, default = zeros. VV - Structure of validation vectors, default = []. TV - Structure of test vectors, default = [].
% in(:,1)=c1; % in(:,2)=c2; % in(:,3)=c3; % in(:,4)=c4; % in(:,5)=c5; % in(:,6)=c6; % in(:,7)=c7; % in(:,8)=c8; %
续
% c1_max=max(c1); % c5_max=max(c5);
PR - Rx2 matrix of min and max values for R input elements. Si - Size of ith layer, for Nl layers. TFi - Transfer function of ith layer, default = 'tansig'. BTF - Backprop network training function, default = 'trainlm'. BLF - Backprop weight/bias learning function, default =
5 实现
数据处理和准备
把WORD数据转换成TXT文件格式 利用dlmread读取数据 是否进行归一化处理?
生成网络
为调用newff命令做好各种准备
pr矩阵的形成 网络结构确定:网络层数以及每层的神经元
个数 每一层的传输函数的确定
注意参数的含义
进行网络训练
为调用train命令进行数据准备
% c4_max=max(c4); % c8_max=max(c8);
c4_min=min(c4); c8_min=min(c8);
续
pr=[c1_min,c1_max;c2_min, c2_max;c3_min,c3_max;c4_ min,c4_max;c5_min,c5_max; c6_min,c6_max;c7_min,c7_ max;c8_min,c8_max;];
Syntax
[net,tr,Y,E,Pf,Af] = train(NET,P,T,Pi,Ai,VV,TV)
Description
TRAIN trains a network NET according to NET.trainFcn and NET.trainParam.
输入参数说明
MATLAB中的神经网络及其应 用:以BP为例
主讲：王茂芝 副教授
1 一个预测问题
已知:一组标准输入和输出数据(见附件) 求解:预测另外一组输入对应的输出 背景:略
2 BP网络
3 MATLAB中的newff命令
NEWFF Create a feed-forward backpropagation network.
输入样本的确定 标准输出的确定 网络训练参数(次数)的确定
net. trainParam.epochs=100
调用网络训练命令:net=train(net,p,t);
进行输出模拟
调用y=sim(net,p)进行输出模拟 画图进行对比
查看网络参数及权值
net net参数引用和查看
Syntax
net = newff net = newff(PR,[S1 S2...SNl],{TF1
TF2...TFNl},BTF,BLF,PF)
命令newff中的参数说明
NET = NEWFF creates a new network with a dialog box.
NEWFF(PR,[S1 S2...SNl],{TF1 TF2...TFNl},BTF,BLF,PF) takes,
输出参数说明
and returns,
NET - New network. TR - Training record (epoch and perf). Y - Network outputs. E - Network errors. Pf - Final input delay conditions. Af - Final layer delay conditions.
说明
Note that T is optional and need only be used for networks that require targets.
Pi and Pf are also optional and need only be used for networks that have input or layer delays.
p=in'; t=out';
net.trainParam.epochs=100; net=train(net,p,t); y=sim(net,p);
The performance function can be any of the differentiable performance functions such as MSE or MSEREG.
4 MATLAB中的train命令
TRAIN Train a neural network.
输入参数数据结构说明
The cell array format is easiest to describe. It is most convenient for networks with multiple inputs and outputs, and allows sequences of inputs to be presented:
输入参数数据结构说明
Where:
Ni = net.numInputs Nl = net.numLayers Nt = net.numTargets ID = net.numInputDelays LD = net.numLayerDelays TS = number of time steps Q = batch size Ri = net.inputs{i}.size Si = yers{i}.size Vi = net.targets{i}.size
'learngdm'. PF - Performance function, default = 'mse'.
and returns an N layer feed-forward backprop network.
参数说明
The transfer functions TFi can be any differentiable transfer function such as TANSIG, LOGSIG, or PURELIN.
6 预测及分析
sim输出 重新训练并sim输出 画图对比
7 程序实现
clc clear all clear net load data; load data_pre;
c1=in(:,1); c2=in(:,2); c3=in(:,3); c4=in(:,4); c5=in(:,5); c6=in(:,6); c7=in(:,7); c8=in(:,8);
The training function BTF can be any of the backprop training functions suபைடு நூலகம்h as TRAINLM, TRAINBFG, TRAINRP, TRAINGD, etc.
参数说明
*WARNING*: TRAINLM is the default training function because it is very fast, but it requires a lot of memory to run. If you get an "out-of-memory" error when training try doing one of these: