A Time Delay Neural Network Based Technique for Nonlinear Microwave Device Modeling

被引：18

作者：

Liu, Wenyuan ^{[1
]}

Zhu, Lin ^{[2
]}

Feng, Feng ^{[3
]}

Zhang, Wei ^{[3
]}

Zhang, Qi-Jun ^{[3
]}

Lin, Qian ^{[4
]}

Liu, Gaohua ^{[5
]}

机构：

[1] Shaanxi Univ Sci & Technol, Sch Elect Informat & Artificial Intelligence, Xian 710021, Peoples R China

[2] Tianjin Chengjian Univ, Sch Control & Mech Engn, Tianjin 300384, Peoples R China

[3] Carleton Univ, Dept Elect, Ottawa, ON K1S 5B6, Canada

[4] Qinghai Univ Nationalities, Coll Phys & Elect Informat Engn, Xining 810007, Peoples R China

[5] Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China

来源：

MICROMACHINES | 2020年 / 11卷 / 09期

基金：

中国国家自然科学基金;

关键词：

nonlinear device modeling; neural networks; optimization methods; EM OPTIMIZATION; EXTRACTION; FILTER;

D O I：

10.3390/mi11090831

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

This paper presents a nonlinear microwave device modeling technique that is based on time delay neural network (TDNN). The proposed technique can accurately model the nonlinear microwave devices when compared to static neural network modeling method. A new formulation is developed to allow for the proposed TDNN model to be trained with DC, small-signal, and large signal data, which can enhance the generalization of the device model. An algorithm is formulated to train the proposed TDNN model efficiently. This proposed technique is verified by GaAs metal-semiconductor-field-effect transistor (MESFET), and GaAs high-electron mobility transistor (HEMT) examples. These two examples demonstrate that the proposed TDNN is an efficient and valid approach for modeling various types of nonlinear microwave devices.

引用

页数：14

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