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Advances of Neural Network Modeling Methods for RF/Microwave Applications

机译：射频/微波应用的神经网络建模方法的进展

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摘要

This paper provides an overview of recent advances of neural network modeling techniques which are very useful for RF/microwave modeling and design. First, we review neural network inverse modeling method for fast microwave design. Conventionally, design parameters are obtained using optimization techniques by multiple evaluations of EM-based models, which take a long time. To avoid this problem, neural network inverse models are developed in a special way, such that they provide design parameters quickly for a given specification. The method is used to design complex waveguide dual mode filters and design parameters are obtained faster than the conventional EM-based technique while retaining comparable accuracy. We also review recurrent neural network (RNN) and dynamic neural network (DNN) methods. Both RNN and DNN structures have the dynamic modeling capabilities and can be trained to learn the analog nonlinear behaviors of the original microwave circuits from input-output dynamic signals. The trained neural networks become fast and accurate behavioral models that can be subsequently used in system-level simulation and design replacing the CPU-intensive detailed representations. Examples of amplifier and mixer behavioral modeling using the neural-network-based approach are also presented.
机译：本文概述了神经网络建模技术的最新进展，这些技术对于RF /微波建模和设计非常有用。首先，我们回顾了用于快速微波设计的神经网络逆建模方法。通常，使用优化技术通过对基于EM的模型进行多次评估来获得设计参数，这需要很长时间。为了避免这个问题，神经网络逆模型以一种特殊的方式开发，使得它们可以快速提供给定规格的设计参数。该方法用于设计复杂的波导双模滤波器，并且比传统的基于EM的技术更快地获得设计参数，同时保持相当的精度。我们还将回顾递归神经网络（RNN）和动态神经网络（DNN）的方法。 RNN和DNN结构都具有动态建模功能，可以通过训练从输入输出动态信号中了解原始微波电路的模拟非线性行为。训练有素的神经网络成为快速而准确的行为模型，可随后用于系统级仿真和设计中，取代CPU密集的详细表示。还介绍了使用基于神经网络的方法进行放大器和混频器行为建模的示例。

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