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Introduction to Neural Network toolbox in Matlab

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 prompte.g. sparc76.cs.c

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Introduction to Neural Network toolbox in Matlab

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    1. Introduction to Neural Network toolbox in Matlab 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

    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

    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));

    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.

    11. Matrix manipulation 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];

    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.

    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.

    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.

    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.

    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.

    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.

    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

    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/

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