The Modeling and Prediction of Strapdown Inertial Measurement Unit based on Support Vector Regression and Particle Swarm Optimization

被引：0
|
作者：
Li, Shuying [1 ]
Xuan Diwu [1 ]
Li, Yiran [1 ]
机构：
[1] Xian Univ Posts & Telecommun, Sch Automat, Xian, Peoples R China
来源：
2019 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2019) | 2019年
基金：
中国国家自然科学基金;
关键词：
support vector regression; particle swarm optimization; stability prediction;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Strapdown inertial measurement unit (SIMU) installed in a vehicle determines the navigation accuracy of the vehicle. However, the stability of SIMU changes with the storage time passing by, which influences the navigation accuracy of the vehicle. Thus, it is very necessary to establish a predication model which can predict the stability of SIMU and evaluate the performance of SIMU. This paper proposes a method to predict the SIMU stability. This algorithm is based on support vector regression and particle swarm optimization. The results expressed that support vector regression and particle swarm optimization can forecast the changing trend of SIMU accurately, which can provide the theoretical basis for evaluating the SIMU's performance.
引用
收藏
页码：1314 / 1318
页数：5
相关论文
共 50 条
  • [1] PM10 Prediction Model by Support Vector Regression Based on Particle Swarm Optimization
    Arampongsanuwat, Saowalak
    Meesad, Phayung
    [J]. FUTURE INFORMATION TECHNOLOGY, 2011, 13 : 189 - 194
  • [2] Support Vector Regression Based on the Particle Swarm Optimization Algorithm for Tight Oil Recovery Prediction
    Huang, Shihui
    Tian, Leng
    Zhang, Jinshui
    Chai, Xiaolong
    Wang, Hengli
    Zhang, Hongling
    [J]. ACS OMEGA, 2021, 6 (47): : 32142 - 32150
  • [3] PM10 Prediction Model by Support Vector Regression Based on Particle Swarm Optimization
    Arampongsanuwat, Saowalak
    Meesad, Phayung
    [J]. MEMS, NANO AND SMART SYSTEMS, PTS 1-6, 2012, 403-408 : 3693 - +
  • [4] Immune Particle Swarm Optimization for Support Vector Regression on Forest Fire Prediction
    Wang, Yan
    Wang, Juexin
    Du, Wei
    Wang, Chuncai
    Liang, Yanchun
    Zhou, Chunguang
    Huang, Lan
    [J]. ADVANCES IN NEURAL NETWORKS - ISNN 2009, PT 2, PROCEEDINGS, 2009, 5552 : 382 - +
  • [5] Dynamic Modeling of SOFC Based on Support Vector Regression Machine and Improved Particle Swarm Optimization
    Huo, Haibo
    Ji, Yi
    Kuang, Xinghong
    Liu, Yuqing
    Wu, Yanxiang
    [J]. 2014 11TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA), 2014, : 1853 - 1858
  • [6] Software Reliability Prediction based on Support Vector Regression with Binary Particle Swarm Optimization for Model Mining
    Insanittaqwa, Vika F.
    Rochimah, Siti
    [J]. 2016 1ST INTERNATIONAL SEMINAR ON APPLICATION FOR TECHNOLOGY OF INFORMATION AND COMMUNICATION (ISEMANTIC): SCIENCE AND TECHNOLOGY FOR A BETTER FUTURE, 2016, : 300 - 305
  • [7] Bandwidth prediction based on Nu-support vector regression and parallel hybrid particle swarm optimization
    Hu L.
    Che X.
    Cheng X.
    [J]. International Journal of Computational Intelligence Systems, 2010, 3 (1) : 70 - 83
  • [8] Bandwidth Prediction based on Nu-Support Vector Regression and Parallel Hybrid Particle Swarm Optimization
    Hu, Liang
    Che, Xilong
    Cheng, Xiaochun
    [J]. INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, 2010, 3 (01) : 70 - 83
  • [9] Parameters Optimization of Support Vector Regression Based on Immune Particle Swarm Optimization Algorithm
    Wang, Yan
    Wang, Juexin
    Du, Wei
    Zhang, Chen
    Zhang, Yu
    Zhou, Chunguang
    [J]. WORLD SUMMIT ON GENETIC AND EVOLUTIONARY COMPUTATION (GEC 09), 2009, : 997 - 1000
  • [10] Particle Swarm Optimization Based Support Vector Regression for Blind Image Restoration
    Dash, Ratnakar
    Sa, Pankaj Kumar
    Majhi, Banshidhar
    [J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 2012, 27 (05): : 989 - 995
12345