Beitrag in einem Tagungsband
Neural Network Modeling of Nonlinear Filters for EMC Simulation in Discrete Time Domain
Details zur Publikation
Autor(inn)en: | Roche, J.; Friebe, J.; Niggemann, O. |
Herausgeber: | IEEE |
Verlag: | IEEE Computer Society |
Verlagsort / Veröffentlichungsort: | United States |
Publikationsjahr: | 2021 |
Seitenbereich: | 1-7 |
Buchtitel: | IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society |
Titel der Buchreihe: | Proceedings of the Annual Conference of the IEEE Industrial Electronics Society |
ISBN: | 978-1-6654-0256-9 |
DOI-Link der Erstveröffentlichung: |
Zusammenfassung, Abstract
Optimization loops are often required for the improvement of function and electromagnetic compatibility (EMC) in a product development process. Such optimization can be realized either by simulations or high effort based measurements. The neural network approach is suitable to overcome typical issues of simulation programs like SPICE such as convergence problems and high computation time. This paper addresses a neural network modeling approach for nonlinear passive filters. Long Short-Term Memory (LSTM) networks are applied to model nonlinear passive filters. One measured and two simulated filter circuits are used as application examples. LSTMs are chosen by literature research as a suitable modeling approach. The neural network and training structure is defined by literature research and systematic experiments. The filter behaviors are basically modeled by the trained neural networks. But further improvements have to be done. It is shown that the corresponding voltage and current time series can be learned and predicted by the LSTM networks in their essential characteristics. These voltage and current time series can generally be used in further applications. A possible speed advantage of LSTM networks is also examined.
Optimization loops are often required for the improvement of function and electromagnetic compatibility (EMC) in a product development process. Such optimization can be realized either by simulations or high effort based measurements. The neural network approach is suitable to overcome typical issues of simulation programs like SPICE such as convergence problems and high computation time. This paper addresses a neural network modeling approach for nonlinear passive filters. Long Short-Term Memory (LSTM) networks are applied to model nonlinear passive filters. One measured and two simulated filter circuits are used as application examples. LSTMs are chosen by literature research as a suitable modeling approach. The neural network and training structure is defined by literature research and systematic experiments. The filter behaviors are basically modeled by the trained neural networks. But further improvements have to be done. It is shown that the corresponding voltage and current time series can be learned and predicted by the LSTM networks in their essential characteristics. These voltage and current time series can generally be used in further applications. A possible speed advantage of LSTM networks is also examined.
Schlagwörter
emc lstm modeling neural network nonlinear filter prediction python simulation spectrum spice time series
