Introduction to Neural Network
toolbox in Matlab
Matlab stands for MATrix LABoratory.
Matlab 5.3.1 with toolboxs.
• SIMULINK
• Signal Processing Toolbox
• Control System Toolbox
• System Identification Toolbox
• Robust Control Toolbox
• Spline Toolbox
• Optimization Toolbox
• Neural Network Toolbox
• Image Processing Toolbox
• Symbolic Math Toolbox
• Fuzzy Logic Toolbox
• Statistics Toolbox
• Wavelet Toolbox
• Communication Toolbox
• Database Toolbox
Programming Language : Matlab
 High-level script language with interpreter.
 Huge library of function and scripts.
 Act as an computing environment that combines
numeric computation, advanced graphics and
visualization.
Entrance of matlab
Type matlab in unix command prompt
• e.g. sparc76.cs.cuhk.hk:/uac/gds/username> matlab
• If you will find an command prompt ‘>>’ and you have
successfully entered matlab.
>>
Ask more information about
software
>> info
– contacting the company
• eg. Technique support, bugs.
>> ver
– version of matlab and its toolboxes
– licence number
>> whatsnew
– what’s new of the version
Function for programmer
 help : Detail of function provided.
– >> help nnet, help sumsqr
 lookfor : Find out a function by giving some
keyword.
– >> lookfor sum
•TRACE Sum of diagonal elements.
•CUMSUM Cumulative sum of elements.
•SUM Sum of elements.
•SUMMER Shades of green and yellow colormap.
•UIRESUME Resume execution of blocked M-file.
•UIWAIT Block execution and wait for resume.
……………...
Function for programmer (cont’d)
which : the location of function in the system
(similar to whereis in unix shell)
– >> which sum
So that you can save it
– sum is a built-in function.
– >> which sumsqr
in your own directory and
modify it.
– /opt1/matlab-5.3.1/toolbox/nnet/nnet/sumsqr.m
Function for programmer (cont’d)
! : calling unix command in matlab system
– >> !ls
– >> !netscape
Plotting graph
 Visualisation of the data and result.
 Most important when handing in the report.
 plot : plot the vector in 2D or 3D
– >> y = [1 2 3 4]; figure(1); plot(power(y,2));
Index of the vector (you
can make another vector
for the x-axis)
x = [2 4 6 8];
plot(x,power(y,2));
Add vector x as the
x-axis index
Implementation of Neural
Network using NN Toolbox
Version 3.0.1
 1. Loading data source.
 2. Selecting attributes required.
 3. Decide training, validation, and testing data.
 4. Data manipulations and Target generation.
– (for supervised learning)
 5. Neural Network creation (selection of network
architecture) and initialisation.
 6. Network Training and Testing.
 7. Performance evaluation.
Loading data
load: retrieve data from disk.Save variables in matlab
environment and load back
– In ascii or .mat format.
>> data = load(‘wtest.txt’);
>> whos data;
Name
Size
Bytes Class
data 826x7
46256 double array
Matrix manipulation
for all
stockname = data(:,1);
Start for 1
training = data([1:100],:)
a=[1;2]; a*a’ => [1,2;2,4];
1
2
a=[1,2;2,4]; a.*a => [1,4;4,16];
1
4
4
16
2
4
Neural Network Creation and
Initialisation
 net = newff(PR,[S1 S2...SNl],{TF1 TF2...TFNl},BTF,BLF,PF) S2: number of
ouput neuron
 Description
 NEWFF(PR,[S1 S2...SNl],{TF1 TF2...TFNl},BTF,BLF,PF) takes,
 PR - Rx2 matrix of min and max values for R input elements.
S1: number
hidden neurons
 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 = 'learngdm'.
 PF - Performance function, default = 'mse’ and returns an
 N layer feed-forward backprop network.
>> PR = [-1 1; -1 1; -1 1; -1 1];
Min
-1
-1
-1
-1
neuron 1
1
1
Number of inputs
1 Max
decided by PR
1
Neural Network Creation
 newff : create a feed-forward network.
 Description
 NEWFF(PR,[S1 S2...SNl],{TF1 TF2...TFNl},BTF,BLF,PF) takes,
 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 = 'learngdm'.
 PF - Performance function, default = 'mse’ and returns an
 N layer feed-forward backprop network.
>> net = newff([-1 1; -1 1; -1 1; -1 1], [4,1], {‘logsig’ ‘logsig’});
TF2: logsig
TF1: logsig
Number of inputs
decided by PR
Network Initialisation
Initialise the net’s weighting and biases
>> net = init(net);
% init is called after newff
re-initialise with other function:
–
–
–
–
net.layers{1}.initFcn = 'initwb';
net.inputWeights{1,1}.initFcn = 'rands';
net.biases{1,1}.initFcn = 'rands';
net.biases{2,1}.initFcn = 'rands';
Network Training
 The overall architecture of your neural network is store in the
variable net;
 We can reset the variable inside.
net.trainParam.epochs =1000;
(Max no. of epochs to train) [100]
net.trainParam.goal =0.01;
(stop training if the error goal hit) [0]
net.trainParam.lr =0.001;
(learning rate, not default trainlm) [0.01]
net.trainParam.show =1;
(no. epochs between showing error) [25]
net.trainParam.time =1000;
(Max time to train in sec) [inf]
Network Training(cont’d)
 train : train the network with its architecture.
 Description
-0.5 1
-1
0.5
0.5 1
-0.5 -1
 TRAIN(NET,P,T,Pi,Ai) takes,

NET - Network.
Training

P - Network inputs.
pattern 1

T - Network targets, default = zeros.

Pi - Initial input delay conditions, default = zeros.

Ai - Initial layer delay conditions, default = zeros.
-0.5
-1
0.5
-0.5
1
0.5
1
-1
>> p = [-0.5 1 -0.5 1; -1 0.5 -1 0.5; 0.5 1 0.5 1; -0.5 -1 -0.5 -1];
For
neuron 1
Network Training(cont’d)
 train : train the network with its architecture.
 Description
-1
1
-1
 TRAIN(NET,P,T,Pi,Ai) takes,

NET - Network.
Training

P - Network inputs.
pattern 1

T - Network targets, default = zeros.

Pi - Initial input delay conditions, default = zeros.

Ai - Initial layer delay conditions, default = zeros.
>> p = [-0.5 1 -0.5 1; -1 0.5 -1 0.5; 0.5 1 0.5 1; -0.5 -1 -0.5 -1];
>> t = [-1 1 -1 1];
>> net = train(net, p, t);
1
Simulation of the network
 [Y] = SIM(model, UT)
 Y
 model
 UT
: Returned output in matrix or structure format.
: Name of a block diagram model.
: For table inputs, the input to the model is interpolated.
Training
pattern 1
-0.5
-0.25
-1.00
-1.00
>> UT = [-0.5 1 ; -0.25 1; -1 0.25 ; -1 0.5];
>> Y = sim(net,UT);
1.00
1.00
0.25
0.50
For
neuron 1
Performance Evaluation
 Comparison between target and network’s output in
testing set.(generalisation ability)
 Comparison between target and network’s output in
training set. (memorisation ability)
 Design a function to measure the distance/similarity
of the target and output, or simply use mse for
example.
Write them in a file
(Adding a new function)
Create a file as fname.m (extension as .m)
>> fname
function [Y , Z] = othername(str)
Y = load(str);
Z = length(Y);
>> [A,B] = loading('wtest.txt');
loading.m
Reference
Neural Networks Toolbox User's Guide
– http://www.cse.cuhk.edu.hk/corner/tech/doc/manual/matlab-5.3.1/help/pdf_doc/nnet/nnet.pdf
Matlab Help Desk
– http://www.cse.cuhk.edu.hk/corner/tech/doc/manual/matlab-5.3.1/help/helpdesk.html
Mathworks ower of Matlab
– http://www.mathworks.com/