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首页> 外文期刊>International journal of RF and microwave computer-aided engineering >A New Neural Network Technique for the Design of Multilayered Microwave Shielded Bandpass Filters
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A New Neural Network Technique for the Design of Multilayered Microwave Shielded Bandpass Filters

机译：多层微波屏蔽带通滤波器设计的新神经网络技术

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摘要

In this work, we propose a novel technique based on neural networks, for the design of microwave filters in shielded printed technology. The technique uses radial basis function neural networks to represent the non linear relations between the quality factors and coupling coefficients, with the geometrical dimensions of the resonators. The radial basis function neural networks are employed for the first time in the design task of shielded printed filters, and permit a fast and precise operation with only a limited set of training data. Thanks to a new cascade configuration, a set of two neural networks provide the dimensions of the complete filter in a fast and accurate way. To improve the calculation of the geometrical dimensions, the neural networks can take as inputs both electrical parameters and physical dimensions computed by other neural networks. The neural network technique is combined with gradient based optimization methods to further improve the response of the filters. Results are presented to demonstrate the usefulness of the proposed technique for the design of practical microwave printed coupled line and hairpin filters.
机译：在这项工作中，我们提出了一种基于神经网络的新颖技术，用于屏蔽印刷技术中的微波滤波器的设计。该技术使用径向基函数神经网络来表示品质因数和耦合系数之间的非线性关系，以及谐振器的几何尺寸。径向基函数神经网络在屏蔽印刷滤波器的设计任务中首次被采用，并且仅使用有限的一组训练数据就可以进行快速而精确的操作。得益于新的级联配置，两个神经网络组成的一组以快速，准确的方式提供了完整过滤器的尺寸。为了改进几何尺寸的计算，神经网络可以将其他神经网络计算出的电参数和物理尺寸作为输入。神经网络技术与基于梯度的优化方法相结合，进一步改善了滤波器的响应。结果表明了提出的技术对设计实用的微波印刷耦合线和发夹滤波器的实用性。

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