Quantification of Uncontrolled Large Variations of Parameters on System Performance via Support Vector Machine Surrogates

Trinchero, R.; Canavero, F. G.

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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

Titolo:	Quantification of Uncontrolled Large Variations of Parameters on System Performance via Support Vector Machine Surrogates
Autori:	TRINCHERO, RICCARDO CANAVERO, Flavio
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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Utilizza questo identificativo per citare o creare un link a questo documento:
http://hdl.handle.net/11583/2768133

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