Yan Shuxia, Qian Fengqi, Li Chenglin, Wang Jian, Wang Xu, Liu Wenyuan
School of Electronics and Information Engineering, Tiangong University, Tianjin 300387, China.
School of Microelectronics, Tianjin University, Tianjin 300072, China.
Micromachines (Basel). 2023 Aug 14;14(8):1600. doi: 10.3390/mi14081600.
In this paper, an improved empirical formula modeling method using neuro-space mapping (Neuro-SM) for coupled microstrip lines is proposed. Empirical formulas with correction values are used for the coarse model, avoiding a slow trial-and-error process. The proposed model uses mapping neural networks (MNNs), including both geometric variables and frequency variables to improve accuracy with fewer variables. Additionally, an advanced method incorporating simple sensitivity analysis expressions into the training process is proposed to accelerate the optimization process. The experimental results show that the proposed model with its simple structure and an effective training process can accurately reflect the performance of coupled microstrip lines. The proposed model is more compatible than models in existing simulation software.
本文提出了一种基于神经空间映射（Neuro-SM）的改进经验公式建模方法，用于耦合微带线。粗模型使用带有校正值的经验公式，避免了缓慢的试错过程。所提出的模型使用映射神经网络（MNN），包括几何变量和频率变量，以用更少的变量提高精度。此外，还提出了一种将简单灵敏度分析表达式纳入训练过程的先进方法，以加速优化过程。实验结果表明，所提出的模型结构简单且训练过程有效，能够准确反映耦合微带线的性能。所提出的模型比现有仿真软件中的模型更具兼容性。
