Archivio Istituzionale della RicercaThis paper investigates the application of the support vector machine and the least-squares support vector machine regressions to the uncertainty quantification of complex systems. The feasibility and the accuracy of the above techniques are demonstrated by predicting the efficiency of an integrated voltage regulator with 8 stochastic parameters
SVM and LS-SVM for the Uncertainty Quantification of Complex Systems / Trinchero, R.; Canavero, F. G.. - ELETTRONICO. - (2018). ( 4th Workshop Uncertainty Modeling for Engineering Applications Split, Croatia 10-11th December 2018).
SVM and LS-SVM for the Uncertainty Quantification of Complex Systems
R. Trinchero;F. G. Canavero
2018
Abstract
This paper investigates the application of the support vector machine and the least-squares support vector machine regressions to the uncertainty quantification of complex systems. The feasibility and the accuracy of the above techniques are demonstrated by predicting the efficiency of an integrated voltage regulator with 8 stochastic parameters
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UMEMA18_SVM_IVR_final.pdf
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Descrizione: submitted paper
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https://hdl.handle.net/11583/2768135
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