Application of Improved SROM Based on RBF Neural Network Model in EMC Worst Case Estimation

The Stochastic Reduced-Order Models (SROMs) are a non-embedded uncertainty analysis method that has the advantages of high computational efficiency, easy implementation, and no dimensional disasters. Recently, it has been widely used in the field of EMC simulation. In the process of optimizing elect...

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Bibliographic Details
Published in:Progress In Electromagnetics Research Letters 2024, Vol.119, p.51
Main Authors: Hu, Bing, Wang, Yingxin, Huo, Shenghang, Bai, Jinjun
Format: Report
Language:English
Subjects:
Algorithms
Electromagnetic compatibility
Electromagnetism
Monte Carlo method
Neural networks
Online Access:Get full text
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Summary:The Stochastic Reduced-Order Models (SROMs) are a non-embedded uncertainty analysis method that has the advantages of high computational efficiency, easy implementation, and no dimensional disasters. Recently, it has been widely used in the field of EMC simulation. In the process of optimizing electromagnetic protection design, the worst-case estimation value is an extremely important uncertainty quantification simulation result. However, the SROMs have a large error in providing this result, which limits its application in the field of EMC simulation prediction. An improved SROM based on the Radial Basis Function (RBF) neural network algorithm is proposed in this paper, which improves the fitness function in the genetic algorithm center clustering process and constructs an RBF neural network model to obtain accurate worst-case estimation results. The accuracy improvement effect of the algorithm proposed in this paper in worst-case estimation is quantitatively verified by using a parallel cable crosstalk prediction example from published literature.
ISSN:1937-6480
1937-6480
DOI:10.2528/PIERL24012503