This paper presents a preliminary application of the support vector machine regression to the modeling and the statistical assessment of system responses affected by large uncertainties. The support vector machine regression is applied to generate a robust and compact surrogate model of the system output from a limited set of randomly selected training samples. The system surrogate can be suitably adopted both for design optimization and for stochastic analysis. The feasibility, accuracy and robustness to noise of the surrogate model calculated via the support vector machine regression are investigated by considering a realistic printed circuit board interconnect structure
Quantification of Uncontrolled Large Variations of Parameters on System Performance via Support Vector Machine Surrogates / Trinchero, R.; Canavero, F. G.. - ELETTRONICO. - (2018). ((Intervento presentato al convegno 19ème Colloque International et Exposition sur la Compatibilité ÉlectroMagnétique (CEM 2018).
Titolo: | Quantification of Uncontrolled Large Variations of Parameters on System Performance via Support Vector Machine Surrogates |
Autori: | |
Data di pubblicazione: | 2018 |
Abstract: | This paper presents a preliminary application of the support vector machine regression to the mod...eling and the statistical assessment of system responses affected by large uncertainties. The support vector machine regression is applied to generate a robust and compact surrogate model of the system output from a limited set of randomly selected training samples. The system surrogate can be suitably adopted both for design optimization and for stochastic analysis. The feasibility, accuracy and robustness to noise of the surrogate model calculated via the support vector machine regression are investigated by considering a realistic printed circuit board interconnect structure |
Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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http://hdl.handle.net/11583/2768133