I've created this model by editing the codes from the toolbox. The purpose of this model is to train the network with operating data from a turbine. the data is normalized and then the target will be set according to the actual fault occurrence which tagged as "1" and during normal operation "0". I will be comparing the result of several training function, the number of neuron, the number of layers, and activation function.
% This script assumes these variables are defined: % data - input data. % target - target data. % load data load data.mat; load target.mat; x = data; t = target; % Choose a Training Function % For a list of all training functions type: help nntrain % 'trainlm' is usually fastest. % 'trainbr' takes longer but may be better for challenging problems. % 'trainscg' uses less memory. NFTOOL falls back to this in low memory situations. trainFcn = 'trainbr'; % Bayesian Regularization % Create a Feedforward Network hiddenLayerSize = 18; net = feedforwardnet (hiddenLayerSize,trainFcn); % Setup Division of Data for Training, Validation, Testing RandStream.setGlobalStream(RandStream('mt19937ar','seed',1)); % to get constant result net.divideFcn = 'divideblock'; % Divide targets into three sets using blocks of indices net.divideParam.trainRatio = 70/100; net.divideParam.valRatio = 15/100; net.divideParam.testRatio = 15/100; %TRAINING PARAMETERS net.trainParam.show=50; %# of ephocs in display net.trainParam.lr=0.05; %learning rate net.trainParam.epochs=10000; %max epochs net.trainParam.goal=0.05^2; %training goal net.performFcn='mse'; %Name of a network performance function %type help nnperformance % Train the Network [net,tr] = train(net,x,t); % Test the Network y = net(x); e = gsubtract(t,y); performance = perform(net,t,y) % View the Network view(net)
The questions are: Is it correct to use this code below and will it affect the function of my model?
RandStream.setGlobalStream(RandStream('mt19937ar','seed',1)); % to get constant result
ANSWER
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1. Use FITNET (calls FEEDFORWARDNET) for regression and curve-fitting 2. Use PATTERNNET (calls FEEDFORWARDNET) for classification and pattern-recognition 3. You have a classification problem. Start with the simple code in help patternnet doc patternnet 4. If there are c classes, the target matrix columns should be columns of eye(c): O = c. 5. The relationship between trueclass indices 1:c and the target columns is target = ind2vec(trueclassindices); trueclassindices = vec2ind(target);
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