Dynamic Frequency-Temperature Characteristic Modeling for Quartz Crystal Resonator Based on Improved Echo State Network

被引:3
作者
Deng, Xiaogang [1 ]
Wang, Shubin [1 ]
Jing, Shengjie [1 ]
Huang, Xianri [2 ]
Huang, Weixing [2 ]
Cui, Baochun [2 ,3 ]
机构
[1] China Univ Petr, Coll Control Sci & Engn, Qingdao 266580, Peoples R China
[2] Qingdao ChryStar Elect Technol Ltd, Qingdao 266580, Peoples R China
[3] Shandong Univ Sci & Technol, Coll Elect Engn & Automat, Qingdao 266580, Peoples R China
关键词
Crystal resonator; dynamic modeling; echo state network (ESN); frequency-temperature (f-T) characteristic; ENERGY;
D O I
10.1109/TUFFC.2021.3118929
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
Quartz crystal resonators are the key component of various kinds of electronic systems because they provide the reference frequency source of the system running clocks. However, the frequency stability is often affected by the temperature. Therefore, the frequency-temperature (f-T) characteristic modeling has been an important research topic in the frequency control field. The classic f- T modeling method omits the system dynamics and may lead to a large frequency compensation error in the case of rapid temperature changing. To deal with this issue, this article proposes a dynamic f- T modeling method based on improved echo state network (ESN), called residual scaled ESN (RSESN). In the proposed method, the residual modeling framework is designed for purposes of good physical understandability and high prediction precision. This framework uses the static polynomial f- T model to depict the approximated data relationship and applies the complicated network model to compensate the detailed dynamic error. To estimate the dynamic errors, one effective dynamic modeling tool, ESN, is introduced to build the dynamic compensation model for f- T characteristic of quartz crystal resonators. For a better fitting performance, the ESN activation limitations are analyzed and the scaled echo states are constructed in the improved ESN model. The modeling and testing results on the real experiment data show that the proposed method can capture the dynamic information effectively and provide better frequency deviation predictions.
引用
收藏
页码:438 / 446
页数:9
相关论文
共 37 条
[1]   The Moore-Penrose Pseudoinverse: A Tutorial Review of the Theory [J].
Alves Barata, Joao Carlos ;
Hussein, Mahir Saleh .
BRAZILIAN JOURNAL OF PHYSICS, 2012, 42 (1-2) :146-165
[2]   Echo State Networks for data-driven downhole pressure estimation in gas-lift oil wells [J].
Antonelo, Eric A. ;
Camponogara, Eduardo ;
Foss, Bjarne .
NEURAL NETWORKS, 2017, 85 :106-117
[3]   STATIC AND DYNAMIC BEHAVIOR OF QUARTZ RESONATORS [J].
BALLATO, A .
IEEE TRANSACTIONS ON SONICS AND ULTRASONICS, 1979, 26 (04) :299-306
[4]   Online adaptive dynamic programming based on echo state networks for dissolved oxygen control [J].
Bo Ying-Chun ;
Zhang Xin .
APPLIED SOFT COMPUTING, 2018, 62 :830-839
[5]   A Novel Design of DTCXO with Low Phase Noise [J].
Chen, Faxi ;
Zhou, Wei ;
Zhao, Jie .
2010 IEEE INTERNATIONAL FREQUENCY CONTROL SYMPOSIUM (FCS), 2010, :435-436
[6]   Frequency and amplitude modulations in crystal resonators due to transient thermal effects [J].
Cui, Jing ;
Du, Jianke ;
Wang, Ji ;
Yang, Jiashi .
JOURNAL OF APPLIED PHYSICS, 2014, 115 (05)
[7]   Modified Modeling Method of Quartz Crystal Resonator Frequency-Temperature Characteristic With Considering Thermal Hysteresis [J].
Deng, Xiaogang ;
Wang, Shubin ;
Huang, Xianri ;
Liu, Hao ;
Cui, Baochun .
IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL, 2021, 68 (03) :890-898
[8]  
Esterline JC, 2012, P IEEE INT FREQ CONT
[9]   Interval Type-2 Fuzzy Neural Networks for Chaotic Time Series Prediction: A Concise Overview [J].
Han, Min ;
Zhong, Kai ;
Qiu, Tie ;
Han, Bing .
IEEE TRANSACTIONS ON CYBERNETICS, 2019, 49 (07) :2720-2731
[10]  
Hsieh WL, 2018, P IEEE INT FREQ CONT, P291