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Machine-learning-based hybrid random-fuzzy uncertainty quantification for EMC and SI assessment

Simon De Ridder, Domenico Spina (UGent) , Nicola Toscani, Flavia Grassi, Dries Vande Ginste (UGent) and Tom Dhaene (UGent)
(2020) IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY. 62(6). p.2538-2546
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Abstract
Modeling the effects of uncertainty is of crucial importance in the signal integrity and Electromagnetic Compatibility assessment of electronic products. In this article, a novel machine-learning-based approach for uncertainty quantification problems involving both random and epistemic variables is presented. The proposed methodology leverages evidence theory to represent probabilistic and epistemic uncertainties in a common framework. Then, Bayesian optimization is used to efficiently propagate this hybrid uncertainty on the performance of the system under study. Two suitable application examples validate the accuracy and efficiency of the proposed method.
Keywords
POLYNOMIAL-CHAOS, VARIABILITY ANALYSIS, GLOBAL OPTIMIZATION, MULTIPORT, SYSTEMS, Uncertainty, Electromagnetic compatibility, Computational modeling, Probabilistic logic, Optimization, Probability density function, Probability distribution, Bayesian optimization, epistemic uncertainty, random-fuzzy problems

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MLA
De Ridder, Simon, et al. “Machine-Learning-Based Hybrid Random-Fuzzy Uncertainty Quantification for EMC and SI Assessment.” IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY, vol. 62, no. 6, 2020, pp. 2538–46, doi:10.1109/TEMC.2020.2980790.
APA
De Ridder, S., Spina, D., Toscani, N., Grassi, F., Vande Ginste, D., & Dhaene, T. (2020). Machine-learning-based hybrid random-fuzzy uncertainty quantification for EMC and SI assessment. IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY, 62(6), 2538–2546. https://doi.org/10.1109/TEMC.2020.2980790
Chicago author-date
De Ridder, Simon, Domenico Spina, Nicola Toscani, Flavia Grassi, Dries Vande Ginste, and Tom Dhaene. 2020. “Machine-Learning-Based Hybrid Random-Fuzzy Uncertainty Quantification for EMC and SI Assessment.” IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY 62 (6): 2538–46. https://doi.org/10.1109/TEMC.2020.2980790.
Chicago author-date (all authors)
De Ridder, Simon, Domenico Spina, Nicola Toscani, Flavia Grassi, Dries Vande Ginste, and Tom Dhaene. 2020. “Machine-Learning-Based Hybrid Random-Fuzzy Uncertainty Quantification for EMC and SI Assessment.” IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY 62 (6): 2538–2546. doi:10.1109/TEMC.2020.2980790.
Vancouver
1.
De Ridder S, Spina D, Toscani N, Grassi F, Vande Ginste D, Dhaene T. Machine-learning-based hybrid random-fuzzy uncertainty quantification for EMC and SI assessment. IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY. 2020;62(6):2538–46.
IEEE
[1]
S. De Ridder, D. Spina, N. Toscani, F. Grassi, D. Vande Ginste, and T. Dhaene, “Machine-learning-based hybrid random-fuzzy uncertainty quantification for EMC and SI assessment,” IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY, vol. 62, no. 6, pp. 2538–2546, 2020.
@article{8686244,
  abstract     = {{Modeling the effects of uncertainty is of crucial importance in the signal integrity and Electromagnetic Compatibility assessment of electronic products. In this article, a novel machine-learning-based approach for uncertainty quantification problems involving both random and epistemic variables is presented. The proposed methodology leverages evidence theory to represent probabilistic and epistemic uncertainties in a common framework. Then, Bayesian optimization is used to efficiently propagate this hybrid uncertainty on the performance of the system under study. Two suitable application examples validate the accuracy and efficiency of the proposed method.}},
  author       = {{De Ridder, Simon and Spina, Domenico and Toscani, Nicola and Grassi, Flavia and Vande Ginste, Dries and Dhaene, Tom}},
  issn         = {{0018-9375}},
  journal      = {{IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY}},
  keywords     = {{POLYNOMIAL-CHAOS,VARIABILITY ANALYSIS,GLOBAL OPTIMIZATION,MULTIPORT,SYSTEMS,Uncertainty,Electromagnetic compatibility,Computational modeling,Probabilistic logic,Optimization,Probability density function,Probability distribution,Bayesian optimization,epistemic uncertainty,random-fuzzy problems}},
  language     = {{eng}},
  number       = {{6}},
  pages        = {{2538--2546}},
  title        = {{Machine-learning-based hybrid random-fuzzy uncertainty quantification for EMC and SI assessment}},
  url          = {{http://doi.org/10.1109/TEMC.2020.2980790}},
  volume       = {{62}},
  year         = {{2020}},
}
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