基于先验知识的微带耦合谐振滤波器神经网络建模

IF 0.6 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC Facta Universitatis-Series Electronics and Energetics Pub Date : 2022-01-01 DOI:10.2298/fuee2202145m
Z. Marinković, Milos Mitic, Branka Milosevic, M. Nedelchev
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引用次数: 0

摘要

微带耦合谐振器滤波器的设计包括滤波器谐振器单元之间耦合系数的确定。本文提出了一种利用先验知识神经网络方法的新型建模方法,作为标准电磁仿真和纯基于人工神经网络的神经模型的有效替代方法。与纯粹基于人工神经网络的模型相比,它具有与EM模拟相似的精度,并且需要更少的训练数据和更少的模型开发时间。
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Prior knowledge based neural modeling of microstrip coupled resonator filters
The design of microstrip coupled resonator filters includes determination of the coupling coefficients between the filter resonator units. In this paper a novel modeling procedure exploiting prior knowledge neural approach is proposed as an efficient alternative to the standard electromagnetic (EM) simulations and to the neural models based purely on the artificial neural networks (ANNs). It has similar accuracy as the EM simulations and requires less training data and less time needed for the model development than the models based purely on ANNs.
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来源期刊
Facta Universitatis-Series Electronics and Energetics
Facta Universitatis-Series Electronics and Energetics ENGINEERING, ELECTRICAL & ELECTRONIC-
自引率
16.70%
发文量
10
审稿时长
20 weeks
期刊最新文献
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