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Neural network of calibrated coarse model and application to substrate integrated waveguide filter design

机译：校准粗模型的神经网络及应用于基板集成波导滤波器设计

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In this article, we propose a novel neural network of calibrated coarse model, which can obtain the optimal filter response with as little training data as possible to synthesize the entire substrate integrated waveguide (SIW) filter. By incorporating the knowledge of filter decomposition with the inverse neural network, we build a coarse model that can synthesize the dimensions of a SIW filter. However, the SIW structures are subject to a potential leakage problem due to the periodic gaps, the results of the coarse model are very different from the ideal response. We propose a novel calibrated neural network from the perspective of the coupling matrix to correct the errors generated in the coarse model. In addition, this article also proposes an equivalent de-embedding technique, which is simpler than the thru-reflect-line calibration technique to accurately extract the scattering parameters of the SIW discontinuities. An Hplane fifth order SIW filter is synthesized by the proposed model. The result shows that the SIW filter that is very close to the ideal response can be synthesized with only a few hundred training data.
机译：在本文中，我们提出了一种校准粗略模型的新型神经网络，其可以获得尽可能少的训练数据来获得最佳滤波器响应，以合成整个基板集成波导（SiW）滤波器。通过将滤波器分解的知识与逆神经网络合并，我们构建了一个粗略模型，可以合成SIW滤波器的尺寸。然而，由于周期性间隙，SiW结构受到潜在的泄漏问题，因此粗略模型的结果与理想响应非常不同。我们从耦合矩阵的角度提出了一种新型校准神经网络，以校正粗略模型中产生的错误。此外，本文还提出了一种等效的去嵌入技术，该技术比通过直接反射线校准技术更简单，以精确提取SiW不连续性的散射参数。通过所提出的模型合成HPLANE第五阶SIW滤波器。结果表明，非常接近理想响应的SIW滤波器可以仅用几百次训练数据合成。

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