Efficient Implementation of the Vector-Valued Kernel Ridge Regression for the Uncertainty Quantification of the Scattering Parameters of a 2-GHz Low-Noise Amplifier

被引：1

作者：

Soleimani, Nastaran ^{[1
]}

Manfredi, Paolo ^{[1
]}

Trinchero, Riccardo ^{[1
]}

机构：

[1] Politecn Torino, Turin, Italy

来源：

2023 IEEE MTT-S INTERNATIONAL CONFERENCE ON NUMERICAL ELECTROMAGNETIC AND MULTIPHYSICS MODELING AND OPTIMIZATION, NEMO | 2023年

关键词：

Machine learning; kernel machine; vector-valued KRR; stochastic analysis; amplifiers;

D O I：

10.1109/NEMO56117.2023.10202518

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper focuses on the application of an efficient implementation of the vector-valued kernel Ridge regression (KRR) to the uncertainty quantification (UQ) of the scattering parameters of a low-noise amplifier (LNA). Specifically, the performance of the proposed technique have been investigated for the statistical assessment of the mean value, variance and probability density function (PDF) of the S-11 and S-21 parameters of a 2-GHz LNA induced by 25 stochastic input parameters and compared with the corresponding reference results computed via a plain Monte Carlo (MC) simulation.

引用

页码：143 / 146

页数：4