Precision of a radial basis function neural network tracking method

被引：2
|
作者：
Hanan, J [1 ]
Zhou, HY [1 ]
Chao, TH [1 ]
机构：
[1] Jet Prop Lab, Pasadena, CA 91109 USA
来源：
OPTICAL PATTERN RECOGNITION XIV | 2003年 / 5106卷
关键词：
autonomous tracking; neural network; target recognition; data reduction;
D O I：
10.1117/12.501406
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The precision of a radial basis function (RBF) neural network based tracking method has been assessed against real targets. Intensity profile feature extraction was used to build a model in real time, evolving with the target. Precision was assessed against traditionally measured frame-by-frame measurements from the recorded data set. The results show the potential limit for the technique and reveal intricacies associated with empirical data not necessarily observed in simulations.
引用
收藏
页码：146 / 153
页数：8
相关论文
共 50 条
  • [21] Analysis of algorithms for radial basis function neural network
    Stastny, Jiri
    Skorpil, Vladislav
    PERSONAL WIRELESS COMMUNICATIONS, 2007, 245 : 54 - +
  • [22] Coordinate transformation by radial basis function neural network
    Gullu, Mevlut
    SCIENTIFIC RESEARCH AND ESSAYS, 2010, 5 (20): : 3141 - 3146
  • [23] A Novel Reformulated Radial Basis Function Neural Network
    Yin, Jianchuan
    Hu, Jiangqiang
    Bu, Renxiang
    CCDC 2009: 21ST CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-6, PROCEEDINGS, 2009, : 2997 - 3001
  • [24] Radial basis function neural network for a traffic model
    Luo, Zanwen
    Wu, Zhijian
    Han, Zengjin
    Qinghua Daxue Xuebao/Journal of Tsinghua University, 2001, 41 (09): : 106 - 110
  • [25] Chaotic Neural Network with Radial Basis Function Disturbance
    Xu, Nan
    Xu, Yao-Qun
    ELEVENTH WUHAN INTERNATIONAL CONFERENCE ON E-BUSINESS, 2012, : 668 - +
  • [26] Kalman Filtering Compensated by Radial Basis Function Neural Network for Seam Tracking of Laser Welding
    Gao, Xiangdong
    Zhong, Xungao
    You, Deyong
    Katayama, Seiji
    IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2013, 21 (05) : 1916 - 1923
  • [27] Dynamic tracking control of mobile robots using an improved radial basis function neural network
    Liu, Shirong
    Yu, Qijiang
    Yu, Jinshou
    ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 2, PROCEEDINGS, 2006, 3972 : 1172 - 1177
  • [28] Digital Design of Radial Basis Function Neural Network and Recurrent Neural Network
    Sahithya, P.
    Arulmozhi, M.
    Praveen, Nandini
    2019 INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, SIGNAL PROCESSING AND NETWORKING (WISPNET 2019): ADVANCING WIRELESS AND MOBILE COMMUNICATIONS TECHNOLOGIES FOR 2020 INFORMATION SOCIETY, 2019, : 393 - 397
  • [29] LOW-ANGLE RADAR TRACKING USING RADIAL BASIS FUNCTION NEURAL-NETWORK
    WONG, T
    LO, T
    LEUNG, H
    LITVA, J
    BOSSE, E
    IEE PROCEEDINGS-F RADAR AND SIGNAL PROCESSING, 1993, 140 (05) : 323 - 328
  • [30] Radial Basis Function Neural Network Method of Determining Functional Relationships for Quality Function Deployment
    Li Xin
    Huang Lu-cheng
    2009 INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE & ENGINEERING (16TH), VOLS I AND II, CONFERENCE PROCEEDINGS, 2009, : 176 - 182
12345