Xu Long1; Yan Yihua1; Yu Xuexin1; Zhang Weiqiang2; Chen Jie3; Duan Lingyu3
Source Publicationresearchinastronomyandastrophysics
AbstractA 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.
Document Type期刊论文
First Author AffilicationNational Astronomical Observatories, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Xu Long,Yan Yihua,Yu Xuexin,et al. lstmneuralnetworkforsolarradiospectrumclassification[J]. researchinastronomyandastrophysics,2019,19(9).
APA Xu Long,Yan Yihua,Yu Xuexin,Zhang Weiqiang,Chen Jie,&Duan Lingyu.(2019).lstmneuralnetworkforsolarradiospectrumclassification.researchinastronomyandastrophysics,19(9).
MLA Xu Long,et al."lstmneuralnetworkforsolarradiospectrumclassification".researchinastronomyandastrophysics 19.9(2019).
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