A Pruning Method for Neural Networks and Its Application for Optimization in Electromagnetics
Frederico Gadelha Guimarães, Jaime Arturo Ramírez · IEEE Transactions on Magnetics · 2004
In this paper, we propose a method for the exact computation of the Hessian matrix of the training error function for a multilayer perceptron network. The Hessian matrix is divided into small submatrices, which are calculated independently and then assembled. We developed a new pruning technique using the Hessian to estimate the error deviation due to the elimination of connections in the network. The method proposed is applied in the optimization of a loudspeaker's magnet problem consisting of seven design variables. The number of input variables is reduced while achieving the objective of the problem at an acceptable computational time.