I am very new to the field of AI so please bear with me. Say there is a dice with three sides, -1,0 and 1, and I want to predict which side it lands on (so only one output is needed I guess). The input variables are numerous but not that many, maybe 7-10.
These input variables are certain formulae that involve calculations to do with wind, time, angle, momentum etc, and each formula returns which side it thinks the dice will like roll. Let's say that intuitively, by looking at these variables, I can make a very good guess at which side the dice lands on. If for example 6 out of 7 input variables say it likely that the dice will land on 1 but the 7th input suggests that it will land on 0, I would guess it lands on 1. As a human, I'm essentially consulting these inputs as a kind of "brains trust", and I act as a judge to make the final decision based on the brains trust. Of course in that example, my logic as a judge was simply majority rules, but what if some other more complicated non-linear method of judging was needed?
I essentially want my neural network to take this role as a judge. I have read that feedforward nns have limitations regarding control flow and loops, so I'm not sure if that structure will be appropriate. I'm not sure if recurrent nn will be appropriate either as I don't care what the previous inputs were.