Details
| Original language | English |
|---|---|
| Title of host publication | 2018 IEEE Symposium on Electromagnetic Compatibility, Signal Integrity and Power Integrity, EMC, SI and PI 2018 |
| Pages | 165-169 |
| ISBN (electronic) | 9781538666210 |
| Publication status | Published - 17 Oct 2018 |
| Event | 2018 IEE Symposium on Electromagnetic Compatibility, Signal Integrity and Power Integrity (EMC, SI & PI) - Long Beach, United States Duration: 30 Jul 2018 → 3 Aug 2018 |
Abstract
Electromagnetic examinations are usually very expensive. Every simulation needs time for computation and every measurement needs time for preparation. In addition, similar results are generally expected when examining similar objects. If the relation between the differences of investigated objects and their results could be found, a prediction on objects which are not yet examined would be possible. In this paper, a method based on artificial neural networks will be presented, with which a prediction of simulation results of similar objects is possible.
Keywords
- artificial neural network, backpropagation, prediction
ASJC Scopus subject areas
- Engineering(all)
- Safety, Risk, Reliability and Quality
- Computer Science(all)
- Signal Processing
- Energy(all)
- Energy Engineering and Power Technology
- Engineering(all)
- Electrical and Electronic Engineering
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2018 IEEE Symposium on Electromagnetic Compatibility, Signal Integrity and Power Integrity, EMC, SI and PI 2018. 2018. p. 165-169 8495246.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research
}
TY - GEN
T1 - Introduction of Artificial Neural Networks in EMC
AU - Burghardt, Felix
AU - Garbe, Heyno
N1 - Funding information: The results shown in this paper were partly produced with the support of the Bundeswehr Research Institute for Protective Technologies – NBC-Protection in Munster, Germany. Contract Number E/E590/GZ004/CF011
PY - 2018/10/17
Y1 - 2018/10/17
N2 - Electromagnetic examinations are usually very expensive. Every simulation needs time for computation and every measurement needs time for preparation. In addition, similar results are generally expected when examining similar objects. If the relation between the differences of investigated objects and their results could be found, a prediction on objects which are not yet examined would be possible. In this paper, a method based on artificial neural networks will be presented, with which a prediction of simulation results of similar objects is possible.
AB - Electromagnetic examinations are usually very expensive. Every simulation needs time for computation and every measurement needs time for preparation. In addition, similar results are generally expected when examining similar objects. If the relation between the differences of investigated objects and their results could be found, a prediction on objects which are not yet examined would be possible. In this paper, a method based on artificial neural networks will be presented, with which a prediction of simulation results of similar objects is possible.
KW - artificial neural network
KW - backpropagation
KW - prediction
UR - http://www.scopus.com/inward/record.url?scp=85056136353&partnerID=8YFLogxK
U2 - 10.1109/EMCSI.2018.8495246
DO - 10.1109/EMCSI.2018.8495246
M3 - Conference contribution
SP - 165
EP - 169
BT - 2018 IEEE Symposium on Electromagnetic Compatibility, Signal Integrity and Power Integrity, EMC, SI and PI 2018
T2 - 2018 IEE Symposium on Electromagnetic Compatibility, Signal Integrity and Power Integrity (EMC, SI & PI)
Y2 - 30 July 2018 through 3 August 2018
ER -