NDLI: A pruning method for neural networks and its application for optimization in electromagnetics

Content Provider	IEEE Xplore Digital Library
Author	Guimaraes, F.G. Ramirez, J.A.
Copyright Year	1965
Abstract	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.
Sponsorship	IEEE Magnetics Society
Starting Page	1160
Ending Page	1163
Page Count	4
File Size	271062
File Format	PDF
ISSN	00189464
Volume Number	40
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-03-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Optimization methods Intelligent networks Design optimization Input variables Multilayer perceptrons Artificial neural networks Network topology Electromagnetic modeling Sensitivity analysis
Content Type	Text
Resource Type	Article
Subject	Electronic, Optical and Magnetic Materials Electrical and Electronic Engineering

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A pruning method for neural networks and its application for optimization in electromagnetics