The ann program doesn't run all 50 epochs that specified.

Here is the code:

inputs = readmatrix('C:\Users\tanmo\Downloads\Input-Station-2.xlsx');
targets = readmatrix('C:\Users\tanmo\Downloads\Target-Station-2.xlsx');
inputs = inputs';  % Transpose if necessary
targets = targets';  % Transpose if necessary
net = feedforwardnet([10 10]);
net.trainFcn = 'trainlm';  % Using Levenberg-Marquardt backpropagation
net.trainParam.epochs = 50;
net.trainParam.lr = 0.01;
net.trainParam.max_fail = 50;  % Maximum validation failures
net.divideParam.trainRatio = 0.70;
net.divideParam.valRatio = 0.15;
net.divideParam.testRatio = 0.15;
net.trainParam.goal = 0;  % Essentially remove the performance goal// added later
net.trainParam.min_grad = 1e-10;  % Set a very small gradient goal

[net, tr]= train(net, inputs, targets);
outputs = net(inputs);
performance = perform(net, targets, outputs);
plotperform(tr)

I can't fix the number of iteration or epoch. It does a random iteration in different time and plots graph for random epoch values.

Answer 1

[Jayanti]

When you are training the neural network, it may not run till the specified number of epochs if any of the stopping criteria are satisfied. There is possibility of the gradient value to fall below "min_grad" value 1e-10 as a result the training process will stop before running for every epoch.

From the attached image, you can see that the gradient value is less than the “min_grad” value at 15 epoch. Also, you can see the message minimum gradient reached at the bottom of the “nntraintool” dialog box.

To solve this issue, try increasing the value of "min_grad" or avoid specifying it.

Hope this helps!