Currently I am working on a simple, single hidden layer, neural network to estimate the parameters of a certain phenomenon. The network requires two inputs and estimates the single parameter.
The data set used for training is obtained from experiments. This data set contains two input variables and a single output/measurement. The inputs are clean whereas the output/measurement is known to be noisy. Goal for the neural network therefore is to model the surface of the output with respect to the inputs.
Part of this data set is obtained through different experiments and has very little (say, negligible) noise. This part of the data set is clustered at the centre of the measurements and accounts for ~1% of all data points. Furthermore, the density of data points is much less in this patch than it is outside of the patch.
My question, how should I treat this patch with data which essentially yield more information about the true behaviour of the data.
Thanks in advance!