Hello everyone! I would like to create a neural network with 5 input nodes. In the following I have created a simple code with the help of the neural network toolbox. I have a question regarding this code.
How can i improve the performance of network as i use different training algorithm (trainlm and trainscg) with different transfer function(logsig and tansig) in hidden layer, but the best results obtained are only 0.64 MSE and 0.35 R by using trainlm and tansig.
Here is my code:
x = rinputs; t = rtargetfourthroot; % 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. Suitable in low memory situations.
trainFcn = 'trainlm'; % Levenberg-Marquardt backpropagation.
% Create a Fitting Network
hiddenLayerSize = 5; net = fitnet(hiddenLayerSize,trainFcn); % Selection of internal transfer functions
net.layers{1}.transferFcn = 'tansig'; net.layers{2}.transferFcn = 'purelin'; % Choose Input and Output Pre/Post-Processing Functions
% For a list of all processing functions type: help nnprocess
net.input.processFcns = {'removeconstantrows','mapminmax'}; net.output.processFcns = {'removeconstantrows','mapminmax'}; % Setup Division of Data for Training, Validation, Testing
% For a list of all data division functions type: help nndivide
net.divideFcn = 'dividerand'; % Divide data randomly
net.divideMode = 'sample'; % Divide up every sample
net.divideParam.trainRatio = 60/100; net.divideParam.valRatio = 20/100; net.divideParam.testRatio = 20/100; % Choose a Performance Function
% For a list of all performance functions type: help nnperformance
net.performFcn = 'mse'; % Mean Squared Error
% Choose Plot Functions
% For a list of all plot functions type: help nnplot
net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ... 'plotregression', 'plotfit'}; % Train the Network
[net,tr] = train(net,x,t); % Test the Network
y = net(x); e = gsubtract(t,y); performance = perform(net,t,y) % Recalculate Training, Validation and Test Performance
trainTargets = t .* tr.trainMask{1}; valTargets = t .* tr.valMask{1}; testTargets = t .* tr.testMask{1}; trainPerformance = perform(net,trainTargets,y) valPerformance = perform(net,valTargets,y) testPerformance = perform(net,testTargets,y)Thank you
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