"Method of Detection Abnormal Features in Ionosphere Critical Frequency Data on the Basis of Wavelet Transformation and Neural Networks Combination"
written by O. V. Mandrikova, Yu. A. Polozov, V. V. Bogdanov, E. A. Zhizhikina,
published by Journal of Software Engineering and Applications, Vol.5 No.12B, 2012
has been cited by the following article(s):
  • Google Scholar
  • CrossRef
[1] Forecasting of Dissolved Gases in Power Transformer Oil Based on DOG-LSSVM Regression and Artificial Bee Colony
2018
[2] Algorithms of ionospheric anomalies detection in “Aurora” system of operational data analysis
2018
[3] Joint analysis of the ionospheric parameters and cosmic ray data during periods of magnetic storms 2015
2017
[4] Statistical characterization of ionosphere anomalies and their relationship to space weather events
Journal of Space Weather and Space Climate, 2016
[5] Methods of analysis of geophysical data during increased solar activity
Pattern Recognition and Image Analysis, 2016
[6] The analysis of ionospheric parameters during periods of solar events and geomagnetic storms
2016
[7] Анализ ионосферных параметров в периоды солнечных событий и геомагнитных бурь
2016
[8] Method for modeling of the components of ionospheric parameter time variations and detection of anomalies in the ionosphere
Earth, Planets and Space, 2015
[9] Ionospheric parameter modelling and anomaly discovery by combining the wavelet transform with autoregressive models
Annals of Geophysics, 2015
[10] Аппроксимация и анализ ионосферных параметров на основе совмещения вейвлет-преобразования с коллективами нейронных сетей
2014
[11] МОДЕЛИРОВАНИЕ И АНАЛИЗ ВРЕМЕННЫХ РЯДОВ СЛОЖНОЙ СТРУКТУРЫ
2014
[12] Выделение аномалий в ионосферных параметрах на основе совмещения кратномасштабного вейвлет-разложения и нейронных сетей
ОВ МАНДрИКОВА, ЮА ПОЛОзОВ - ikir.ru, 2014
[13] Выделение особенностей в параметрах ионосферы в периоды повышенной сейсмической активности на основе коллектива нейронных сетей
2013