Okay, so it’s been a while and I trained an ensemble. Indeed, training more than one NN and averaging the results yields more accurate results. Usually… The error of a single NN in my case is (in Cartesian coordinates) about 2.2 cm, whereas with an ensemble it goes down to 1.5 cm. In the paper I mentioned there the authors report 10x better accuracy with using 6 NN which is definitely not the case for me and I wonder why is that. I can say that I trained my NNs with far more epochs and used the same NN architecture with the same hyperparameters but different seed for random number generator was provided for each NN. Is doing it this way much different form using different hyperparameters for each NN as the authors did and showed in table 3?
One more thing that bugs me in the article is this sentence:
In Eq. (10), the euclidian distance equation has been
given. This equation has been used to calculate the distance
between end effector and the target known as end effector
error. The selection of the best result among neural-network
results in the committee machine has been done using
this equation.
I don’t understand what “selection of the best result” means. During inference we can hardly tell to which one of those NNs the output is closest to so we can’t really “compare” the result to those NNs.