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.
|Number of pages||5|
|Publication status||Published - 1999|
|Event||International Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA|
Duration: 10 Jul 1999 → 16 Jul 1999
|Conference||International Joint Conference on Neural Networks (IJCNN'99)|
|City||Washington, DC, USA|
|Period||10/07/99 → 16/07/99|