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Neural networks for automated classification of ionospheric irregularities in HF radar backscattered signals
Author(s) -
Wing S.,
Greenwald R. A.,
Meng C.I.,
Sigillito V. G.,
Hutton L. V.
Publication year - 2003
Publication title -
radio science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 84
eISSN - 1944-799X
pISSN - 0048-6604
DOI - 10.1029/2003rs002869
Subject(s) - radar , artificial neural network , computer science , feedforward neural network , ionosphere , set (abstract data type) , feed forward , layer (electronics) , task (project management) , pattern recognition (psychology) , artificial intelligence , telecommunications , geology , geophysics , engineering , chemistry , organic chemistry , systems engineering , control engineering , programming language
The classification of high frequency (HF) radar backscattered signals from the ionospheric irregularities (clutters) into those suitable, or not, for further analysis, is a time‐consuming task even by experts in the field. We tested several different feedforward neural networks on this task, investigating the effects of network type (single layer versus multilayer) and number of hidden nodes upon performance. As expected, the multilayer feedforward networks (MLFNs) outperformed the single‐layer networks. The MLFNs achieved performance levels of 100% correct on the training set and up to 98% correct on the testing set. Comparable figures for the single‐layer networks were 94.5% and 92%, respectively. When measures of sensitivity, specificity, and proportion of variance accounted for by the model are considered, the superiority of the MLFNs over the single‐layer networks is much more striking. Our results suggest that such neural networks could aid many HF radar operations such as frequency search, space weather, etc.