Can a power of y (dependent variable) be used in neural network as input? I am using MLP (Multilayer perceptron) for regression and predicting a continuous variable y (which is the waiting time of passengers in an intercity station). Unfortunately, the fit of the model is bad and It does not predict the variable y well. The residuals of the model have ascending linear pattern. I want to know, can I insert a power of y (for example y^2) as an input to my neural network model (my transfer functions are linear and tansig)? I have seen many times that in situations of having an ascending linear pattern of residual in linear regression we can use a power of y as an input to manage the errors. Can we do the same in MLP?
Thanks in advance for your time and consideration.

NO
You will run into trouble when it comes time to make new predictions. If you don't know the $\text{wait_time}$, you cannot calculate $\text{wait_time}^p$.
Nothing will stop you from inputting this into your model when you are writing your code, since (I assume) you have all of the $\text{wait_time}$ values, but it is illegitimate to use $\text{wait_time}$  to predict $\text{wait_time}$.
