Fast Response Prediction Method Based on Bidirectional Long Short-Term Memory for High-Speed Links

被引:8
|
作者
Luo, Yuhuan [1 ]
Chu, Xiuqin [1 ]
Yuan, Haiyue [1 ]
Wei, Tao [1 ]
Wang, Jun [1 ]
Wu, Feng [1 ]
Li, Yushan [1 ]
机构
[1] Xidian Univ, Minist Educ, Key Lab High Speed Circuit Design & EMC, Xian 710071, Peoples R China
基金
中国国家自然科学基金;
关键词
Bidirectional long short-term memory (Bi-LSTM); high-speed link; nonlinearity; recurrent neural network (RNN); signal integrity (SI); EYE DIAGRAM; DESIGN;
D O I
10.1109/TMTT.2022.3233303
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
There is a recognized need to evaluate the performance of high-speed links accurately and quickly with increasing clock frequency. To evaluate the performance of the systems, the transient simulation method and the fast time-domain simulation method are adopted. Unfortunately, neither method can guarantee both the efficiency and accuracy of the results at the same time. In this article, a modeling method that can overcome the shortcomings of both the transient simulation method and the fast time-domain simulation method is proposed for obtaining responses of high-speed links based on bidirectional long short-term memory (Bi-LSTM). Here, the pulse response and response of a certain number of bits are first simulated or measured, which are used to determine training datasets. Then, the Bi-LSTM-based model uses the generated training datasets to build a precise model. Next, the developed model predicts the response of the high-speed link. Compared to the transient simulation method and fast time-domain simulation method, the results show that the proposed method can consider both accuracy and efficiency. In addition, 33 simulation experiments are performed to demonstrate the robustness of the proposed method. Finally, the accuracy of the proposed method is validated by comparing the measured and predicted results of the high-speed links with different nonlinearities.
引用
收藏
页码:2347 / 2359
页数:13
相关论文
共 50 条
  • [1] Urban road speed prediction based on multisource feature bidirectional long short-term memory
    Xiong Z.
    Li H.
    Xiao S.
    Advances in Transportation Studies, 2021, 55 : 265 - 282
  • [2] Time series prediction method based on the bidirectional long short-term memory network
    Guan, Yepeng
    Su, Guangyao
    Sheng, Yi
    Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University, 2024, 51 (03): : 103 - 112
  • [3] Traffic Speed Prediction in Merging Zone of Urban Expressway Based on Bidirectional Long Short-Term Memory Network
    Xie, Jiming
    Xia, Yulan
    Qin, Yaqin
    Zhao, Rongda
    Liu, Bing
    Duan, Guozhong
    Chen, Jinhong
    Xinan Jiaotong Daxue Xuebao/Journal of Southwest Jiaotong University, 2024, 59 (05): : 1235 - 1244
  • [4] Dynamic Doppler prediction in high-speed rail using long short-term memory neural network
    Xiong, Lei
    Zhang, Zhengyu
    Yao, Dongpin
    TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES, 2021, 32 (09)
  • [5] The Forecasting of Train Occupancy Rate on High-Speed Railway Based on Long Short-Term Memory
    Chen, Xiaozhong
    Liu, Jun
    Ma, Minshu
    Lai, Qingying
    Qiao, Qingjie
    CICTP 2019: TRANSPORTATION IN CHINA-CONNECTING THE WORLD, 2019, : 1961 - 1972
  • [6] A sentiment analysis method based on bidirectional long short-term memory networks
    Zhang, Haifei
    Xu, Jian
    Lei, Liting
    Qiu Jianlin
    Alshalabi, Riyad
    APPLIED MATHEMATICS AND NONLINEAR SCIENCES, 2023, 8 (01) : 55 - 68
  • [7] Research on temperature prediction of shearer cable based on bidirectional long short-term memory
    Zhao, Lijuan
    Lin, Guocong
    Wang, Yadong
    Xie, Bo
    Wan, Chuanxu
    Zhang, Hongqiang
    Tian, Shuo
    Bai, Zhongjian
    Zhang, Meichen
    Jin, Xin
    INTERNATIONAL JOURNAL OF THERMAL SCIENCES, 2025, 210
  • [8] Wind Speed and Solar Irradiance Prediction Using a Bidirectional Long Short-Term Memory Model Based on Neural Networks
    Alharbi, Fahad Radhi
    Csala, Denes
    ENERGIES, 2021, 14 (20)
  • [9] Short-term wind speed prediction model based on long short-term memory network with feature extraction
    Zhongda Tian
    Xiyan Yu
    Guokui Feng
    Earth Science Informatics, 2025, 18 (4)
  • [10] Intelligent forecast engine for short-term wind speed prediction based on stacked long short-term memory
    Shahid, Farah
    Zameer, Aneela
    Iqbal, Muhammad Javaid
    NEURAL COMPUTING & APPLICATIONS, 2021, 33 (20): : 13767 - 13783