I'm wondering if it's okay to use the error function mean squared error (MSE) when using the multilayer perceptron for a regression problem where the target data is bimodal.
I standardize the inputs and log transform as well as standardize the target for faster convergence. But even when log transforming and afterwards standardizing the target the target data is not normal.
Is it wrong to use MSE as the error function if the target data is not normal or is there no such assumption for neural networks?
I can't think of any other error function that would be better to use if the assumptions would not be met, is there another error function that could be more appropriate in this case?
Help would be appreciated