1. Introduction to Neural Network toolbox in Matlab
Matlab stands for MATrix LABoratory.
Matlab 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

2. 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.

3. Type matlab in unix command prompt
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.
>>

4. Ask more information about software
contacting the company
eg. Technique support, bugs.
>> ver
version of matlab and its toolboxes
licence number
>> whatsnew
what’s new of the version

5. 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.
……………...

6. Function for programmer (cont’d)
which : the location of function in the system
(similar to whereis in unix shell)
>> which sum
sum is a built-in function.
>> which sumsqr
/opt1/matlab-5.3.1/toolbox/nnet/nnet/sumsqr.m
So that you can save it
in your own directory and
modify it.

7. Function for programmer (cont’d)
! : calling unix command in matlab system
>> !ls
>> !netscape

8. Add vector x as the x-axis index
Plotting graph
Visualisation of the data and result.
Most important when handing in the report.
plot : plot the vector in 2D or 3D
>> y = [ ]; figure(1); plot(power(y,2));
Add vector x as the x-axis index
x = [ ];
plot(x,power(y,2));
Index of the vector (you can make another vector for the x-axis)

9. 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.

10. load: retrieve data from disk.
Loading data
load: retrieve data from disk.
In ascii or .mat format.
Save variables in matlab
environment and load back
>> data = load(‘wtest.txt’);
>> whos data;
Name Size Bytes Class
data x double array

11. Matrix manipulation stockname = data(:,1); training = data([1:100],:)
for all
stockname = data(:,1);
training = data([1:100],:)
a=[1;2]; a*a’ => [1,2;2,4];
a=[1,2;2,4]; a.*a => [1,4;4,16];
Start for 1
1 2
2 4
1 4
4 16

12. Neural Network Creation and Initialisation
net = newff(PR,[S1 S2...SNl],{TF1 TF2...TFNl},BTF,BLF,PF)
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.
S2: number of ouput neuron
S1: number hidden neurons
neuron 1
Min
Max
>> PR = [-1 1; -1 1; -1 1; -1 1];
Number of inputs decided by PR

13. 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.
TF2: logsig
TF1: logsig
>> net = newff([-1 1; -1 1; -1 1; -1 1], [4,1], {‘logsig’ ‘logsig’});
Number of inputs decided by PR

14. 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';

15. 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]

16. Network Training(cont’d)
train : train the network with its architecture.
Description
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.
For neuron 1
Training pattern 1
>> p = [ ; ; ; ];

17. Network Training(cont’d)
train : train the network with its architecture.
Description
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.
Training pattern 1
>> p = [ ; ; ; ];
>> t = [ ];
>> net = train(net, p, t);

18. Simulation of the network
[Y] = SIM(model, UT)
Y : Returned output in matrix or structure format.
model : Name of a block diagram model.
UT : For table inputs, the input to the model is interpolated.
For neuron 1
Training pattern 1
>> UT = [ ; ; ; ];
>> Y = sim(net,UT);

19. 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.

20. Write them in a file (Adding a new function)
Create a file as fname.m (extension as .m)
>> fname
loading.m
function [Y , Z] = othername(str)
Y = load(str);
Z = length(Y);
>> [A,B] = loading('wtest.txt');

21. Neural Networks Toolbox User's Guide Matlab Help Desk
Reference
Neural Networks Toolbox User's Guide
Matlab Help Desk
Mathworks ower of Matlab

22. End.
And Thank you Matlab.