I have process with laser power and velocity as inputs and temperature as output and i want to model the process with a neural network. Later on, an adaptive controller of the process based on model (with prediction) would be a possible future project. So currently I use a PID controller for the laser to reach a certain temperature, the velocity is an external influence. I worked through the Neural Network Toolbox book by Demuth and I made some assumptions that I hope are correct, please help me out with this:
-I consider a NARX to be the right architecture since the upcoming temperature heavily depends on previous temperatures.
-Levenberg-Marquardt is according to the book the most powerful algo for function analysis
- This procedure is the right approach: http://www.mathworks.es/matlabcentral/answers/14970-neural-network-multi-step-ahead-prediction
So I have ran the process 30 times (starting with zero laser power, zero velocity and room temperature) and no I want to feed them into the network. Is the batch mode here the right approach? If not, how do merge the data for the input (I would assume one epoch is one run of the process). Is it even possible to have multiple inputs (power,velocity) in such a network?
Thanks for every bit of information that you can provide! Kind regards, Wompy
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Most neural networks are , in general, multi-input/multi-output. Just interpret the input, hidden and output variables as column vectors. If you go to
you can find a MATLAB dataset that will represent most of the troubling features of your problem.
Practice on that. If you have problems, post relevant code and error messages.
Hope this helps.
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