Hi,
i have been trying the fit the data to a nonlinear model using neural networks in matlab. i have several sets of data. and my code is working fine for some data sets but not for all the data sets.
for some data sets i am able to fit with good regression coefficient.for some sets of data it is giving me a constant value of output (i.e: almost '0' regression coefficient).
this is the architecture of my neural network: Feedforward neural network with back propagation. no of hidden layers-1 no of neurons in a hidden layer -i am varying to see the result in each run
CAN ANYONE POINT OUT WHAT IS GOING WRONG IN MY CODE PLEASE???
clear; %%to load data from excel file
filename='dD.xlsx'; x=xlsread(filename); p=x(:,2:12); t=x(:,1); inputs = p'; targets = t'; % rng(200,'v4');
rng(0) %%Create a Fitting Network
hiddenLayerSize = 5; net = fitnet(hiddenLayerSize); %%Set up Division of Data for Training, Validation, Testing
% net.divideFcn = 'dividetrain'; % No validation or test data
net.divideParam.trainRatio = 100/100; net.divideParam.valRatio = 0/100; net.divideParam.testRatio = 0/100; net.trainFcn = 'trainbr'; % net = configure(net,ptrans,tn);
net.layers{1}.transferFcn = 'logsig'; net.layers{2}.transferFcn = 'purelin'; %%Train the Network
[net,tr] = train(net,inputs,targets); tr.best_epoch; effective_param = tr.gamk; effective_no_of_parameters = effective_param(length(effective_param)); wt_IL=net.IW{1,1}; wt_HL= net.LW{2,1}; bias_IL=net.b{1}; bias_HL=net.b{2}; %%Test the Network
outputs = net(inputs); errors = gsubtract(outputs,targets); performance = perform(net,targets,outputs)
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