Automatic generation of the optimum threshold for parameter weighted pruning in multiple heterogeneous output neural networks
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.
pruning techniques feedforward neural networks multiple output systems satellite data