The use of neural network training function newff in the new MATLAB

the use of Neural network training function newff in the new MATLAB

I. Introduction of the New 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 are a problem consisting of inputs P and targets T to being 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's 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);p lot (p,t,p,y, ' O ')

The network is trained for epochs. Again the network s output is plotted.

· Net.trainParam.epochs = 50;net = Train (net,p,t); Y = Sim (net,p);p lot (p,t,p,y, ' O ')

second, the new version of NEWFF and older NEWFF call syntax comparison

Example1

For example, enter input (6*1000), Output is (4*1000), then

Legacy definitions: NET=NEWFF (Minmax (input), [14,4],{' Tansig ', ' Purelin '}, ' TRAINLM ');

New definition: net=newff (input,output,14,{' tansig ', ' Purelin '}, ' TRAINLM ');

Example2

For example, enter input (6*1000), Output is (4*1000), then

Legacy definitions: NET=NEWFF (Minmax (input), [49,14,4],{' Tansig ', ' tansig ', ' Tansig '}, ' Traingdx ');

New definition: NET=NEWFF (input,output, [49,14], {' Tansig ', ' tansig ', ' Tansig '}, ' Traingdx ');

third, the old version of the NEWFF use method in the new version

Hint: The old version of the defined NEWFF can also be used in the new version, but there will be warnings, warning as follows:

 

WARNING:NEWFF used in an obsolete.
> in Obs_use at 18
In Newff>create_network at 127
In NEWFF at 102
See NEWFF to update calls to the new argument list.

Four, the new version newff and the old edition NEWFF uses the training effect comparison

old version: old usage training many times, but high precision

new version: less training for new usage, but may not reach the required accuracy

This is caused by the following reasons:

The initial values of the weights and thresholds in the program are randomly assigned, so the results of each run will be different, good and bad.
You can use the weights and thresholds of the network that predict good results as the initial values.
You can view the values of net.iw{1,1}, net.lw{2,1}, Net.b{1}, net.b{2}.

now give a complete example

Percent empty environment variable

CLC

Clear

Percent training data forecast data

data=importdata (' test.txt ');

% randomly sorted from 1 to 768

K=rand (1,768);

[M,n]=sort (k);

 

% input/output data

Input=data (:, 1:8);

Output =data (:, 9);