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| % Variable
inputs=Qt ;
targets=C;
% Create Network:
numHiddenNeurons =10;
net = newfit(inputs,targets,numHiddenNeurons);
% divide inputs:
net.divideFcn= 'dividerand';
net.divideParam.trainRatio = 70/100; % Adjust as desired
net.divideParam.valRatio = 15/100; % Adjust as desired
net.divideParam.testRatio = 15/100; % Adjust as desired
% Train and Apply Network:
net.trainFcn= 'trainlm';
net.trainparam.epochs= 200;
net.trainparam.max_fail= 1000;
net.trainparam.min_grad= 1.0000e-010;
net.trainparam.mu_max= 1.0000e+0100;
[net,tr] = train(net,inputs,targets);
outputs = sim(net,inputs);
% Plot
plotperf(tr)
plotregression(targets,outputs); |
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