Professor, Dept. of Environmental Resources Engineering, SUNY ESF

A multi-scale radial basis function neural network

Collaborators: Dr. Giorgos Mountrakis

MSRBF Evaluation
MSRBF Evaluation

Funders: University of Maine covered patent application fees.

Motivation: One of the constraints of radial basis function (RBF) neural networks revolves around the ability to combine local functions of variable spreads. Development of a multi-scale RBF would allow RBFs to exhibit their full potential in regression and classification tasks from multiple disciplines.

Methodology: The underlying idea is to develop a training mechanism that considers both local and global errors when evaluating candidate node functions.

Findings: The initial experiments are very encouraging in terms of accuracy, consistency and repeatability.

Relevant papers: Patent 7,577,626 awarded by the USPTO – Full patent. A manuscript publication is under development.