I have electric machine, which parameters I measure by 10 sensors. 8 of them measures "input" values and 2 of them result (output). I've got tons of historical data of all of these sensors. I built a Neural Network and train it to aproximate 2 outputs given these 8 inputs (simple multilayer perceptron). It works very well (Error is very small). But the question is: I have got trained model of my process. Can I now somehow train this model to give it 2 outputs as inputs and train it to give me the best combination of input values? I thought about autoencoders. I built some of them to recognize anomallies in this system, but I don't actually see how I can implement them to optimize my input values.
Has anybody done something before?
Maybe I should build some kind of generative model, like GAN? to learn network the distribution of particular input values?