2011
DOI: 10.1109/tmag.2010.2091494
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Layer Recurrent Neural Network Solution for an Electromagnetic Interference Problem

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Cited by 32 publications
(13 citation statements)
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“…The boundary conditions necessary for the solution can be determined correctly only in the case that the inner conductor is cylindrical and the outer one is tubular with infinitely large radius 0 [8,17,21]; in the other cases, the boundary conditions are estimated [20] or the outer conductor is not considered. The problematic solved in the present paper is to some extent related to the problematic of calculating induced current densities and earth impedances of overhead and buried conductors [22][23][24], in the solution of which, for example, the neural network artificial intelligence technique [25] can be employed.…”
Section: Comparison Of Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…The boundary conditions necessary for the solution can be determined correctly only in the case that the inner conductor is cylindrical and the outer one is tubular with infinitely large radius 0 [8,17,21]; in the other cases, the boundary conditions are estimated [20] or the outer conductor is not considered. The problematic solved in the present paper is to some extent related to the problematic of calculating induced current densities and earth impedances of overhead and buried conductors [22][23][24], in the solution of which, for example, the neural network artificial intelligence technique [25] can be employed.…”
Section: Comparison Of Resultsmentioning
confidence: 99%
“…The integral in (23), if improper, converges, and its value is finite if the value of the limit in expression (25) does not depend on the choice of the sequence {( , , )}. For ( , , ) = ( 0 , 0 , 0 ) and for ( , , ) = ( 1 , 1 , 1 ), this integral does not converge because the value of the limit in expression (25) depends on the choice of the sequence {( , , )} and equals either ∞ or zero or the limit does not exist.…”
Section: Magnetic Field Of Current Filament-1mentioning
confidence: 99%
“…Some of the results were presented in (Micu et al 2011) but a more detailed study is given in the following.…”
Section: Results Obtained With Recurrent Neural Networkmentioning
confidence: 99%
“…These were applied by the authors in some EPL-MP electromagnetic interferences studies (Micu et al , 2011. The first one is a neural network alternative to the EPL-MP interference problem presented in (Satsios et al 1999a(Satsios et al , 1999b.…”
mentioning
confidence: 99%
“…NN are learning about the chosen subject from the information provided to them, by detecting relationships between input and desired output data, rather than being defined by user [21].…”
Section: Artificial Intelligence Approachmentioning
confidence: 99%