This paper proposes to generate a smart tool that can inherently and effectively capture the results of parameter variations on the system responses of lumped and distributed electrical circuits. This methodology leverages the so-called affine arithmetic and represents parameter-dependent responses in terms of a multivariate polynomial. The affine representation is propagated from input parameters to circuit responses through a suitable redefinition of the basic operations, such as addition, multiplication or matrix inversion, that are involved in the circuit solution. The proposed framework is applied to the frequency-domain analysis of switching converters, and it turns out to be accurate and more efficient than traditional solutions based on Monte Carlo analysis.
Worst-Case analysis of electrical and electronic equipment via affine arithmetic / Ding, Tongyu; Zhang, Liang; Trinchero, Riccardo; Stievano, IGOR SIMONE; Canavero, Flavio. - STAMPA. - (2017), pp. 991-993. (Intervento presentato al convegno 2017 International Conference on Electromagnetics in Advanced Applications (ICEAA) tenutosi a Verona (Italy) nel 11-15 Sep. 2017) [10.1109/ICEAA.2017.8065425].
Worst-Case analysis of electrical and electronic equipment via affine arithmetic
TRINCHERO, RICCARDO;STIEVANO, IGOR SIMONE;CANAVERO, Flavio
2017
Abstract
This paper proposes to generate a smart tool that can inherently and effectively capture the results of parameter variations on the system responses of lumped and distributed electrical circuits. This methodology leverages the so-called affine arithmetic and represents parameter-dependent responses in terms of a multivariate polynomial. The affine representation is propagated from input parameters to circuit responses through a suitable redefinition of the basic operations, such as addition, multiplication or matrix inversion, that are involved in the circuit solution. The proposed framework is applied to the frequency-domain analysis of switching converters, and it turns out to be accurate and more efficient than traditional solutions based on Monte Carlo analysis.File | Dimensione | Formato | |
---|---|---|---|
cnf-2017-ICEAA-AA.pdf
accesso aperto
Descrizione: cnf-2017-ICEAA-AA.pdf
Tipologia:
2. Post-print / Author's Accepted Manuscript
Licenza:
Pubblico - Tutti i diritti riservati
Dimensione
480.65 kB
Formato
Adobe PDF
|
480.65 kB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11583/2687788
Attenzione
Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo