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Introduction to Neural Network toolbox in Matlab

Introduction to Neural Network toolbox in Matlab . 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

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Introduction to Neural Network toolbox in Matlab

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  1. Introduction to Neural Network toolbox in Matlab • 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 Matlab stands for MATrix LABoratory. Matlab 5.3.1 with toolboxs.

  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. 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 • >> info • 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. 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)); Add vector x as the x-axis index x = [2 4 6 8]; 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. 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 826x7 46256 double array

  11. 1 2 2 4 1 4 4 16 Matrix manipulation 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

  12. neuron 1 -1 1 -1 1 -1 1 -1 1 Min Max 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 Number of inputs decided by PR >> PR = [-1 1; -1 1; -1 1; -1 1];

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

  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. For neuron 1 Training pattern 1 -0.5 1 -0.5 1 -1 0.5 -1 0.5 0.5 1 0.5 1 -0.5 -1 -0.5 -1 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. >> 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];

  17. Training pattern 1 -1 1 -1 1 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. >> 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);

  18. For neuron 1 Training pattern 1 -0.5 1.00 -0.25 1.00 -1.00 0.25 -1.00 0.50 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. >> UT = [-0.5 1 ; -0.25 1; -1 0.25 ; -1 0.5]; >> 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. loading.m 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');

  21. 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/

  22. End. And Thank you Matlab.

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