Inverse modeling of microwave components plays an important role in microwave design and diagnosis or tuning. Since the analytical function or formula of the inverse input‐output relationship does not exist and is difficult to obtain, artificial neural network (ANN) becomes an efficient tool to develop inverse models for microwave components. This paper provides an overview of recent advances in neural network‐based inverse modeling techniques for microwave applications. We review two different shallow neural network‐based inverse modeling techniques, including the comprehensive neural network inverse modeling methodology and the multivalued neural network inverse modeling technique. Both techniques address the problem of nonuniqueness in inverse modeling. We also provide an overview of recently developed hybrid deep neural network modeling technique and the application to inverse modeling. For the inverse modeling problem with high‐dimensional inputs, the relationship between the inputs and the outputs of the inverse model will become more complicated and the inverse modeling problem will become harder. The deep neural network becomes a practical choice. The hybrid deep neural network structure is presented. The recently proposed activation function, specifically for microwave application, and a three‐stage deep learning algorithm for training the hybrid deep neural network are reviewed.

中文翻译：

---

基于神经网络的微波建模逆建模技术的最新进展

微波组件的逆建模在微波设计和诊断或调谐中起着重要作用。由于不存在逆函数输入输出关系的解析函数或公式，并且很难获得，因此人工神经网络（ANN）成为开发微波分量逆模型的有效工具。本文概述了微波应用中基于神经网络的逆建模技术的最新进展。我们回顾了两种基于浅层神经网络的逆建模技术，包括全面的神经网络逆建模方法和多值神经网络逆建模技术。两种技术都解决了逆建模中的非唯一性问题。我们还概述了最近开发的混合深度神经网络建模技术及其在逆建模中的应用。对于具有高维输入的逆建模问题，逆模型的输入和输出之间的关系将变得更加复杂，并且逆建模问题将变得更加困难。深度神经网络成为一种实用的选择。提出了混合深度神经网络结构。综述了最近提出的激活函数，特别是针对微波应用的激活函数，以及用于训练混合深度神经网络的三阶段深度学习算法。逆模型的输入和输出之间的关系将变得更加复杂，逆建模问题将变得更加困难。深度神经网络成为一种实用的选择。提出了混合深度神经网络结构。综述了最近提出的激活函数，特别是针对微波应用的激活函数，以及用于训练混合深度神经网络的三阶段深度学习算法。逆模型的输入和输出之间的关系将变得更加复杂，逆建模问题将变得更加困难。深度神经网络成为一种实用的选择。提出了混合深度神经网络结构。综述了最近提出的激活函数，特别是针对微波应用的激活函数，以及用于训练混合深度神经网络的三阶段深度学习算法。