Advances in Artificial Neural Networks for Electromagnetic Parameterized Modeling

被引:0
|
作者
Zhang, Qi-Jun [1 ]
Feng, Feng [2 ]
Na, Weicong [3 ]
机构
[1] Carleton Univ, Dept Elect, Ottawa, ON, Canada
[2] Tianjin Univ, Sch Microelect, Tianjin, Peoples R China
[3] Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China
来源
2021 13TH GLOBAL SYMPOSIUM ON MILLIMETER-WAVES & TERAHERTZ (GSMM) | 2021年
基金
中国国家自然科学基金;
关键词
artificial neural networks; deep neural networks; EM parameterized modeling; forward modeling; inverse modeling; knowledge-based neural networks; neuro-transfer functions;
D O I
10.1109/GSMM53250.2021.9512007
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Electromagnetic (EM) parameterized modeling is important for EM repetitive analysis, such as EM optimization, what if analysis, and yield optimization. An overview of advances in artificial neural networks (ANNs) for EM parameterized modeling is presented in this paper, covering forward/inverse modeling, deep neural networks, knowledge-based neural networks, neuro-transfer functions, and applications for fast EM modeling with varying values in geometrical parameters.
引用
收藏
页数:3
相关论文
共 50 条
  • [41] Advances in artificial neural networks, machine learning and computational intelligence
    Oneto, Luca
    Bunte, Kerstin
    Navarin, Nicolo
    NEUROCOMPUTING, 2022, 470 : 300 - 303
  • [42] Methodological Advances in Artificial Neural Networks for Time Series Forecasting
    Cogollo, M. R.
    Velasquez, J. D.
    IEEE LATIN AMERICA TRANSACTIONS, 2014, 12 (04) : 764 - 771
  • [43] Advances in artificial neural networks, machine learning and computational intelligence
    Oneto, Luca
    Bunte, Kerstin
    Sperduti, Alessandro
    NEUROCOMPUTING, 2020, 416 : 172 - 176
  • [44] Advances in ungauged streamflow prediction using artificial neural networks
    Besaw, Lance E.
    Rizzo, Donna M.
    Bierman, Paul R.
    Hackett, William R.
    JOURNAL OF HYDROLOGY, 2010, 386 (1-4) : 27 - 37
  • [45] Advances in artificial neural networks, machine learning and computational intelligence
    Aiolli, Fabio
    Biehl, Michael
    Oneto, Luca
    NEUROCOMPUTING, 2018, 298 : 1 - 3
  • [46] Counter propagation artificial neural networks modeling of an enantioselectivity of artificial metalloenzymes
    Mazurek, Sylwester
    Ward, Thomas R.
    Novic, Marjana
    MOLECULAR DIVERSITY, 2007, 11 (3-4) : 141 - 152
  • [47] Counter propagation artificial neural networks modeling of an enantioselectivity of artificial metalloenzymes
    Sylwester Mazurek
    Thomas R. Ward
    Marjana Novič
    Molecular Diversity, 2007, 11 : 141 - 152
  • [48] Parameterized Wavelets for Convolutional Neural Networks
    De Silva, D. D. N.
    Vithanage, H. W. M. K.
    Xavier, S. A.
    Piyatilake, I. T. S.
    Fernando, S.
    2020 IEEE INTERNATIONAL CONFERENCE ON AUTONOMOUS ROBOT SYSTEMS AND COMPETITIONS (ICARSC 2020), 2020, : 170 - 176
  • [49] Modeling Viscosity of Volcanic Melts With Artificial Neural Networks
    Langhammer, D.
    Di Genova, D.
    Steinle-Neumann, G.
    GEOCHEMISTRY GEOPHYSICS GEOSYSTEMS, 2022, 23 (12)
  • [50] Cutting force modeling using artificial neural networks
    J Mater Process Technol, (344-349):