The minimum gradient complexity control applied to sensitivity extraction of electromagnetic devices

D. A G Vieira; J. A. Vasconcelos; V. Palade; W. M. Caminhas

doi:10.1109/TMAG.2007.915896

The minimum gradient complexity control applied to sensitivity extraction of electromagnetic devices

D. A G Vieira, J. A. Vasconcelos, V. Palade, W. M. Caminhas

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

This paper applies the parallel layer perceptron network trained with the minimum gradient method (PLP-MGM) to the problem of indirect sensitivity extraction of electromagnetic devices. The networks trained with the MGM are less dependent of users' defined parameters, as, for instance, the number of neurons. Some results are presented considering the indirect sensitivity extraction of a loudspeaker magnet assembly unit and an inverse scattering problem, and they show the effectiveness of the proposed approach.

Original language	English
Article number	4527018
Pages (from-to)	1114-1117
Number of pages	4
Journal	IEEE Transactions on Magnetics
Volume	44
Issue number	6
DOIs	https://doi.org/10.1109/TMAG.2007.915896
Publication status	Published - Jun 2008
Externally published	Yes

Keywords

Neural networks
Optimization and design
Parallel layer perceptron
Regularization methods

ASJC Scopus subject areas

Electrical and Electronic Engineering
Physics and Astronomy (miscellaneous)

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10.1109/TMAG.2007.915896

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abstract = "This paper applies the parallel layer perceptron network trained with the minimum gradient method (PLP-MGM) to the problem of indirect sensitivity extraction of electromagnetic devices. The networks trained with the MGM are less dependent of users' defined parameters, as, for instance, the number of neurons. Some results are presented considering the indirect sensitivity extraction of a loudspeaker magnet assembly unit and an inverse scattering problem, and they show the effectiveness of the proposed approach.",

keywords = "Neural networks, Optimization and design, Parallel layer perceptron, Regularization methods",

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AU - Palade, V.

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AB - This paper applies the parallel layer perceptron network trained with the minimum gradient method (PLP-MGM) to the problem of indirect sensitivity extraction of electromagnetic devices. The networks trained with the MGM are less dependent of users' defined parameters, as, for instance, the number of neurons. Some results are presented considering the indirect sensitivity extraction of a loudspeaker magnet assembly unit and an inverse scattering problem, and they show the effectiveness of the proposed approach.

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KW - Optimization and design

KW - Parallel layer perceptron

KW - Regularization methods

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The minimum gradient complexity control applied to sensitivity extraction of electromagnetic devices

Abstract

Keywords

ASJC Scopus subject areas

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