Method of ionospheric data analysis based on a combination of wavelet transform and neural networks

被引：9
|
作者：
Mandrikova, O. [1 ]
Polozov, Yu. [1 ]
Geppener, V. [2 ]
机构：
[1] RAS, Inst Cosmophys Res & Radio Wave Propagat FEB, Mirnaya Str 7, Paratunka 684034, Kamchatka Regio, Russia
[2] St Petersburg Electrotech Univ LETI, Ul Prof Popova 5, St Petersburg 197376, Russia
来源：
3RD INTERNATIONAL CONFERENCE INFORMATION TECHNOLOGY AND NANOTECHNOLOGY (ITNT-2017) | 2017年 / 201卷
基金：
俄罗斯科学基金会;
关键词：
wavelet-transform; neural networks; critical frequency of the ionosphere; ionospheric storms; anomalies; magnetic storms; MODEL;
D O I：
10.1016/j.proeng.2017.09.622
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：
The paper presents a hybrid system based on a combination of wavelet filtering operations and regression neural networks. The system is adapted to analyze the ionosphere data obtained at "Paratunka" station (Kamchatka). Testing of the system has shown its efficiency in the tasks of analysis of characteristic properties of ionospheric data and detection of anomalies occurring during disturbed periods. For a detailed analysis of anomalies, computing solutions based on the application of continuous wavelet transform and threshold functions were suggested. The developed computational tools were implemented in software environment (http://aurorasa.ikir.ru:8580). (C) 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 3rd International Conference "Information Technology and Nanotechnology".
引用
收藏
页码：756 / 766
页数：11
相关论文
共 50 条
  • [1] Texture image segmentation method based on wavelet transform and neural networks
    Zhang, J
    Oe, S
    [J]. 1998 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, VOLS 1-5, 1998, : 4595 - 4600
  • [2] Modeling and analysis of ionospheric parameters by a combination of wavelet transform and autoregression models
    Mandrikova, O. V.
    Glushkova, N. V.
    Zhivet'ev, I. V.
    [J]. GEOMAGNETISM AND AERONOMY, 2014, 54 (05) : 593 - 600
  • [3] Modeling and analysis of ionospheric parameters by a combination of wavelet transform and autoregression models
    O. V. Mandrikova
    N. V. Glushkova
    I. V. Zhivet’ev
    [J]. Geomagnetism and Aeronomy, 2014, 54 : 593 - 600
  • [4] Optical neural networks based on wavelet transform
    Fan, CL
    Jin, ZH
    Tian, WF
    [J]. OPTICAL INFORMATION PROCESSING TECHNOLOGY, 2002, 4929 : 356 - 363
  • [5] Combination of PCA and Undecimated wavelet transform for neural data processing
    Farashi, Sajad
    Abolhassani, Mohammad D.
    Salimpour, Yousef
    Alirezaie, Javad
    [J]. 2010 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2010, : 6666 - 6669
  • [6] Application of the combination of wavelet transform and neural networks in oscillographic chronopotentiometric determination
    Zhong, HB
    Li, GB
    Liu, H
    Zheng, JB
    Chen, LR
    [J]. CHINESE JOURNAL OF ANALYTICAL CHEMISTRY, 2001, 29 (11) : 1264 - 1268
  • [7] Research advances on combination of wavelet analysis and neural networks
    [J]. 2000, Sci Press (22):
  • [8] Runoff forecast by combination wavelet transform with the back propagation artificial neural networks
    Liu, SY
    Quan, XZ
    Zhang, YC
    [J]. PROCEEDINGS OF THE INTERNATIONAL SYMPOSIUM ON WATER RESOURCES AND THE URBAN ENVIRONMENT, 2003, : 709 - 712
  • [9] Optimization method by combination of wavelet neural networks and genetic algorithm
    School of Aerospace Science and Engineering, Beijing Institute of Technology, Beijing 100081, China
    不详
    [J]. Hangkong Dongli Xuebao, 2008, 11 (1953-1960):
  • [10] Wavelet Transform for the Analysis of Convolutional Neural Networks in Texture Recognition
    Florindo, Joao Batista
    [J]. PROCEEDINGS OF THE 17TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISAPP), VOL 4, 2022, : 502 - 509
12345