In:
Research in Astronomy and Astrophysics, IOP Publishing, Vol. 19, No. 9 ( 2019-09-01), p. 135-
Kurzfassung:
A solar radio spectrometer records solar radio radiation in the radio waveband. Such solar radio radiation spanning multiple frequency channels and over a short time period could provide a solar radio spectrum which is a two dimensional image. The vertical axis of a spectrum represents frequency channel and the horizontal axis signifies time. Intrinsically, time dependence exists between neighboring columns of a spectrum since solar radio radiation varies continuously over time. Thus, a spectrum can be treated as a time series consisting of all columns of a spectrum, while treating it as a general image would lose its time series property. A recurrent neural network (RNN) is designed for time series analysis. It can explore the correlation and interaction between neighboring inputs of a time series by augmenting a loop in a network. This papermakes the first attempt to utilize an RNN, specifically long short-termmemory (LSTM), for solar radio spectrum classification. LSTM can mine well the context of a time series to acquire more information beyond a non-time series model. As such, as demonstrated by our experimental results, LSTM can learn a better representation of a spectrum, and thus contribute better classification.
Materialart:
Online-Ressource
ISSN:
1674-4527
DOI:
10.1088/1674-4527/19/9/135
Sprache:
Unbekannt
Verlag:
IOP Publishing
Publikationsdatum:
2019
ZDB Id:
2511247-8
SSG:
6,25
SSG:
16,12
