基于卷积神经网络的电磁仿真结果可信度评估

IF 0.9 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC
Jinjun Bai;Yulei Liu;Dewu Kong;Kaibin Guo
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引用次数: 0

摘要

电磁仿真结果可信度评估方法的核心思想是取代具有电磁计算专业背景的专家来评估仿真结果的可信度。代表性的算法是由IEEE标准协会提出的特征选择验证(FSV)方法。然而,现有的可信度评估方法都使用统计指标或信号处理方法来模拟专家的真实想法,并没有实现真正的人工智能。在这封信中,提出了一种基于卷积神经网络的模拟结果可信度评估方法,旨在将专家的真实想法(电磁计算的背景知识)融入评估中,而不仅仅是机械数值计算,避免非专业人员造成的评估误差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Credibility Evaluation of Electromagnetic Simulation Results Based on Convolutional Neural Network
The core idea of the credibility evaluation method of electromagnetic simulation results is to replace the experts with an electromagnetic computing professional background to evaluate the credibility of simulation results. The representative algorithm is the feature selective validation (FSV) method proposed by the IEEE Standards Association. However, the existing credibility assessment methods all use statistical indicators or signal processing methods to simulate the real thoughts of experts and have not achieved true artificial intelligence. In this letter, a credibility evaluation method of simulation results based on a convolutional neural network is proposed, which aims to integrate the real ideas of experts (background knowledge of electromagnetic calculation) into the evaluation, instead of just mechanical numerical calculation, and to avoid evaluation errors caused by nonprofessional.
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