High Dimensional Electromagnetic Interference Signal Clustering Based On SOM Neural Network

In this paper, we study the spectral characteristics and global representations of strongly nonlinear, non-stationary electromagnetic interferences (EMI), which is of great significance in analysing the mathematical modelling of electromagnetic capability (EMC) for a large scale integrated system. W...

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Main Authors: Hongyi Li, Di Zhao, Shaofeng Xu, Pidong Wang, Jiaxin Chen
Format: Article
Language:English
Published: University of Banja Luka 2016-06-01
Series:Electronics
Subjects:
Online Access:http://electronics.etfbl.net/journal/Vol20No1/xPaper_05.pdf
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author Hongyi Li
Di Zhao
Shaofeng Xu
Pidong Wang
Jiaxin Chen
author_facet Hongyi Li
Di Zhao
Shaofeng Xu
Pidong Wang
Jiaxin Chen
author_sort Hongyi Li
collection DOAJ
description In this paper, we study the spectral characteristics and global representations of strongly nonlinear, non-stationary electromagnetic interferences (EMI), which is of great significance in analysing the mathematical modelling of electromagnetic capability (EMC) for a large scale integrated system. We firstly propose to use Self-Organizing Feature Map Neural Network (SOM) to cluster EMI signals. To tackle with the high dimensionality of EMI signals, we combine the dimension reduction and clustering approaches, and find out the global features of different interference factors, in order to finally provide precise mathematical simulation models for EMC design, analysis, forecasting and evaluation. Experimental results have demonstrated the validity and effectiveness of the proposed method.
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spelling doaj.art-2834d1f3896b4b66ba8e9d29076e56312022-12-22T04:19:51ZengUniversity of Banja LukaElectronics1450-58432016-06-01201273110.7251/ELS1620027LHigh Dimensional Electromagnetic Interference Signal Clustering Based On SOM Neural NetworkHongyi Li0Di Zhao1Shaofeng Xu2Pidong Wang3Jiaxin Chen4Beihang UniversityBeihang UniversityBeihang UniversityBeihang UniversityBeihang UniversityIn this paper, we study the spectral characteristics and global representations of strongly nonlinear, non-stationary electromagnetic interferences (EMI), which is of great significance in analysing the mathematical modelling of electromagnetic capability (EMC) for a large scale integrated system. We firstly propose to use Self-Organizing Feature Map Neural Network (SOM) to cluster EMI signals. To tackle with the high dimensionality of EMI signals, we combine the dimension reduction and clustering approaches, and find out the global features of different interference factors, in order to finally provide precise mathematical simulation models for EMC design, analysis, forecasting and evaluation. Experimental results have demonstrated the validity and effectiveness of the proposed method.http://electronics.etfbl.net/journal/Vol20No1/xPaper_05.pdfEMImathematical simulation modelsSOM
spellingShingle Hongyi Li
Di Zhao
Shaofeng Xu
Pidong Wang
Jiaxin Chen
High Dimensional Electromagnetic Interference Signal Clustering Based On SOM Neural Network
Electronics
EMI
mathematical simulation models
SOM
title High Dimensional Electromagnetic Interference Signal Clustering Based On SOM Neural Network
title_full High Dimensional Electromagnetic Interference Signal Clustering Based On SOM Neural Network
title_fullStr High Dimensional Electromagnetic Interference Signal Clustering Based On SOM Neural Network
title_full_unstemmed High Dimensional Electromagnetic Interference Signal Clustering Based On SOM Neural Network
title_short High Dimensional Electromagnetic Interference Signal Clustering Based On SOM Neural Network
title_sort high dimensional electromagnetic interference signal clustering based on som neural network
topic EMI
mathematical simulation models
SOM
url http://electronics.etfbl.net/journal/Vol20No1/xPaper_05.pdf
work_keys_str_mv AT hongyili highdimensionalelectromagneticinterferencesignalclusteringbasedonsomneuralnetwork
AT dizhao highdimensionalelectromagneticinterferencesignalclusteringbasedonsomneuralnetwork
AT shaofengxu highdimensionalelectromagneticinterferencesignalclusteringbasedonsomneuralnetwork
AT pidongwang highdimensionalelectromagneticinterferencesignalclusteringbasedonsomneuralnetwork
AT jiaxinchen highdimensionalelectromagneticinterferencesignalclusteringbasedonsomneuralnetwork