Automatic generation of the optimum threshold for parameter weighted pruning in multiple heterogeneous output neural networks

Authors:
A. Luchetta
Affiliations:
Department of Electronics and Telecommunications (DET), University of Florence, Via S. Marta 3, 50139 Florence, Italy
Venue:
Neurocomputing
Year:
2008

Citing 13
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Abstract

In this paper a new procedure for the selection of pruning threshold in feedforward artificial neural networks (FANN) is presented. It is based on an evaluation of a local sensitivity index which has been previously calculated with respect to any single output of the network. Special emphasis has been given to a particular class of neural networks with multiple heterogeneous outputs. The effectiveness of the proposed method will be shown by the development of a neural architecture devoted to a specific multi-output inversion system. The proposed pruning technique provides criteria in deciding ''when'' and ''how much'' to prune the designed neural network.