Neural network inverse modeling and applications to microwave filter design
Neural network inverse modeling and applications to microwave filter design
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DOI：
10.1109/tmtt.2008.919078
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发表时间：
2008-04-01
影响因子：
4.3
通讯作者：
Zhang, Qi-Jun
中科院分区：
文献类型：
--
作者：
Kabir, Humayun;Wang, Ying;Zhang, Qi-Jun
本文利用逆模型的概念，提出了以电学参数为输入、几何参数为输出的微波建模与设计的系统神经网络建模技术。由于逆模型中输入输出关系的不唯一性，直接训练神经网络逆模型可能会变得困难。我们提出了一种新的方法，通过检测训练数据中的多值解来解决这一问题。使用神经网络正向模型根据导数信息将包含多值解的数据分成组，使得各个组不存在多值解的问题。基于划分的数据组建立多个逆模型，然后将其组合成一个完整的模型。提出了一种包括直接逆建模、分段、导数分割和模型组合技术在内的综合建模方法。将该方法应用到波导型滤波器的建模中，与直接神经网络逆建模法相比，得到了更准确的结果。给出了Ku波段圆波导双模伪椭圆带通滤波器的完整电磁仿真和测量结果，验证了神经网络逆建模方法的有效性。
In this paper, systematic neural network modeling techniques are presented for microwave modeling and design using the concept of inverse modeling where the inputs to the inverse model are electrical parameters and outputs are geometrical parameters. Training the neural network inverse model directly may become difficult due to the nonuniqueness of the input-output relationship in the inverse model. We propose a new method to solve such a problem by detecting multivalued solutions in training data. The data containing multivalued solutions are divided into groups according to derivative information using a neural network forward model such that individual groups do not have the problem of multivalued solutions. Multiple inverse models are built based on divided data groups, and are then combined to form a complete model. A comprehensive modeling methodology is proposed, which includes direct inverse modeling, segmentation, derivative division, and model combining techniques. The methodology is applied to waveguide filter modeling and more accurate results are achieved compared to the direct neural network inverse modeling method. Full electromagnetic simulation and measurement results of Ku-band circular waveguide dual-mode pseudoelliptic bandpass filters are presented to demonstrate the efficiency of the proposed neural network inverse modeling methodology.