An adjustable model for linear to nonlinear regression
T. Jan, Anthony Zaknich · 2003
A basic limitation of all data-driven approximation methods is their inability to extrapolate accurately once the input is outside of the training data range. This paper examines the effectiveness and utility of combining a linear regression model with general regression neural network or modified probabilistic neural network for better linear extrapolation and function approximation. For a given set of training data, this combination provides a way of fine tuning the model by the adjustment of a single smoothing parameter as well as providing linear extrapolation.