基于双修正复值神经网络的模拟系统建模

A. Luchetta, S. Manetti, M. C. Piccirilli
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引用次数: 0

摘要

本文的目的是提出一种新的方法来识别一般分布式设备的集总电路模型。它是基于使用双重修正复值神经网络。该方法并不是针对某一类特殊的电磁系统,而是给出了一个完整验证近似集总模型和提取电参数值的步骤。系统的输入是所考虑结构的几何(和/或制造)参数,而输出是集总电路参数。该方法遵循频率响应分析(FRA)方法来详细说明呈现给网络的数据。
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Analog system modeling based on a double modified complex valued neural network
The aim of this work is to present a novel technique for the identification of lumped circuit models of general distributed apparatus and devices. It is based on the use of a double modified complex value neural network. The method is not oriented to a unique class of electromagnetic systems, but it gives a procedure for the complete validation of the approximated lumped model and the extraction of the electrical parameter values. The inputs of the system are the geometrical (and/or manufacturing) parameters of the considered structure, while the outputs are the lumped circuit parameters. The method follows the Frequency Response Analysis (FRA) approach for elaborating the data presented to the network.
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