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Machine Learning for the Performance Assessment of High-Speed Links.
- Source :
-
IEEE Transactions on Electromagnetic Compatibility . Dec2018, Vol. 60 Issue 6, p1627-1634. 8p. - Publication Year :
- 2018
-
Abstract
- This paper investigates the application of support vector machine to the modeling of high-speed interconnects with largely varying and/or highly uncertain design parameters. The proposed method relies on a robust and well-established mathematical framework, yielding accurate surrogates of complex dynamical systems. An identification procedure based on the observation of a small set of system responses allows generating compact parametric relations, which can be used for design optimization and/or stochastic analysis. The feasibility and strength of the method are demonstrated based on a benchmark function and on the statistical assessment of a realistic printed circuit board interconnect, highlighting the main features and benefits of this technique over state-of-the-art solutions. Emphasis is given to the effects of the initial sample size and of input noise on the model estimation. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00189375
- Volume :
- 60
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Electromagnetic Compatibility
- Publication Type :
- Academic Journal
- Accession number :
- 131487599
- Full Text :
- https://doi.org/10.1109/TEMC.2018.2797481