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Artificial neural network modeling of plasmonic transmission lines

机译:等离子体传输线的人工神经网络建模

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摘要

In this paper, new models based on an artificial neural network (ANN) are developed to predict the propagation characteristics of plasmonic nanostrip and coupled nanostrips transmission lines. The trained ANNs are capable of providing the required propagation characteristics with good accuracy and almost instantaneously. The non-linear mapping performed by the trained ANNs is written as closed-form expressions, which facilitate the direct use of the results obtained in this research. The propagation characteristics of the investigated transmission lines include the effective refractive index and the characteristic impedance. The time needed to simulate 1000 different versions of the transmission line structure is about 48 h, using a full-wave electromagnetic solver compared to 3 s using the developed ANN model. (C) 2016 Optical Society of America
机译:在本文中,开发了一种基于人工神经网络(ANN)的新模型来预测等离子体纳米带和耦合纳米带传输线的传播特性。训练有素的人工神经网络能够几乎即时地以高精度提供所需的传播特性。由受过训练的人工神经网络执行的非线性映射被编写为封闭形式的表达式,这有助于直接使用本研究中获得的结果。被研究的传输线的传播特性包括有效折射率和特性阻抗。使用全波电磁求解器模拟1000种不同版本的传输线结构所需的时间约为48小时,而使用已开发的ANN模型则需要3 s。 (C)2016美国眼镜学会

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