I'm trying to use neural network partial least square proposed by Qin and McAvoy. The whole network trained based on the scaled values. I want to know how can I rewrite the general neural network equation (where parameters are trained based on scaled values) so that I can be able to find the prediction for original predictors. The transformation from normalized linear model to standard setting is straight forward, but how is it possible for non-linear models.
Any suggestion would be appreciated. Specially if there exist a function in R that can do the job. In general, is it suggested to scale inputs before using neural net or not?