Knowledge-based artificial neural network models for finline

被引：1
作者：
Li, C [1 ]
Xu, J [1 ]
Xue, LJ [1 ]
机构：
[1] Univ Elect Sci & Technol China, Inst Appl Phys, Chengdu 610054, Peoples R China
来源：
INTERNATIONAL JOURNAL OF INFRARED AND MILLIMETER WAVES | 2001年 / 22卷 / 02期
关键词：
artificial neural network; knowledge based; finline; millimeter wave;
D O I：
10.1023/A:1010760707665
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：
Finline plays an important role in millimeter-wave integrated-circuit design. In this paper, a knowledge-based artificial neural network is used to model the finline. Using prior knowledge input method and Bayesian regularization technique make the neural network models for finline reduce the amount of training data needed and prevent overfitting in neural network training. The neural network is electromagnetically developed with a set of training data that are produced by the finite element method, which is robust both from the angle of time of computation and accuracy.
引用
收藏
页码：351 / 359
页数：9
相关论文
共 50 条
[21]   Overview of artificial neural network models in the biomedical domain [J].
Renganathan, V .
BRATISLAVA MEDICAL JOURNAL-BRATISLAVSKE LEKARSKE LISTY, 2019, 120 (07) :536-540
[22]   Cyberbullying Detection by Using Artificial Neural Network Models [J].
Bozyigit, Alican ;
Utku, Semih ;
Nasiboglu, Efendi .
2019 4TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ENGINEERING (UBMK), 2019, :520-524
[23]   Artificial neural network models for estimating regional reference evapotranspiration based on climate factors [J].
Dai, Xiaoqin ;
Shi, Haibin ;
Li, Yunsheng ;
Ouyang, Zhu ;
Huo, Zailin .
HYDROLOGICAL PROCESSES, 2009, 23 (03) :442-450
[24]   Weighted Mean Temperature Hybrid Models in China Based on Artificial Neural Network Methods [J].
Cai, Meng ;
Li, Junyu ;
Liu, Lilong ;
Huang, Liangke ;
Zhou, Lv ;
Huang, Ling ;
He, Hongchang .
REMOTE SENSING, 2022, 14 (15)
[25]   Methods used for quantifying the prediction uncertainty of artificial neural network based hydrologic models [J].
Kasiviswanathan, K. S. ;
Sudheer, K. P. .
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 2017, 31 (07) :1659-1670
[26]   Spreadsheet Calculators for Stability Number of Armor Units Based on Artificial Neural Network Models [J].
Kim, In-Chul ;
Suh, Kyung-Duck .
KSCE JOURNAL OF CIVIL ENGINEERING, 2019, 23 (12) :4961-4971
[27]   Comparative study of conventional and artificial neural network-based ETo estimation models [J].
M. Kumar ;
A. Bandyopadhyay ;
N. S. Raghuwanshi ;
R. Singh .
Irrigation Science, 2008, 26 :531-545
[28]   Methods used for quantifying the prediction uncertainty of artificial neural network based hydrologic models [J].
K. S. Kasiviswanathan ;
K. P. Sudheer .
Stochastic Environmental Research and Risk Assessment, 2017, 31 :1659-1670
[29]   Spreadsheet Calculators for Stability Number of Armor Units Based on Artificial Neural Network Models [J].
In-Chul Kim ;
Kyung-Duck Suh .
KSCE Journal of Civil Engineering, 2019, 23 :4961-4971
[30]   Portfolio Optimization Based on Artificial Neural Network and GARCH-EVT-Copula Models [J].
Ta, Bao Quoc ;
Khai, Nguyen H. Q. .
INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS, 2023, 31 (SUPP02) :289-306
12345