Adjustable model for linear to nonlinear regression

Tony Jan, Anthony Zaknich

Research output: Contribution to conferencePaperpeer-review

7 Citations (Scopus)


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.

Original languageEnglish
Number of pages5
Publication statusPublished - 1999
Externally publishedYes
EventInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
Duration: 10 Jul 199916 Jul 1999


ConferenceInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA


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