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LSTM neural network for solar radio spectrum classification
Xu, Long1,2; Yan, Yi-Hua1; Yu, Xue-Xin1; Zhang, Wei-Qiang2; Chen, Jie3; Duan, Ling-Yu3
2019-09-01
Source PublicationRESEARCH IN ASTRONOMY AND ASTROPHYSICS
ISSN1674-4527
Volume19Issue:9Pages:12
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 paper makes the first attempt to utilize an RNN, specifically long short-term memory (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.
Keyworddeep learning long short-term memory (LSTM) classification solar radio spectrum solar burst detection
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China ; CAS 100-Talents ; CAS 100-Talents ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; CAS 100-Talents ; CAS 100-Talents ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; CAS 100-Talents ; CAS 100-Talents ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; CAS 100-Talents ; CAS 100-Talents
DOI10.1088/1674-4527/19/9/135
Language英语
Funding ProjectNational Natural Science Foundation of China[61572461] ; National Natural Science Foundation of China[11790305] ; National Natural Science Foundation of China[61811530282] ; National Natural Science Foundation of China[61872429] ; National Natural Science Foundation of China[61661146005] ; National Natural Science Foundation of China[U1611461] ; CAS 100-Talents
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China ; CAS 100-Talents ; CAS 100-Talents ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; CAS 100-Talents ; CAS 100-Talents ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; CAS 100-Talents ; CAS 100-Talents ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; CAS 100-Talents ; CAS 100-Talents
WOS Research AreaAstronomy & Astrophysics
WOS SubjectAstronomy & Astrophysics
WOS IDWOS:000485147000013
PublisherNATL ASTRONOMICAL OBSERVATORIES, CHIN ACAD SCIENCES
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Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/27765
Collection中国科学院国家天文台
Corresponding AuthorXu, Long
Affiliation1.Chinese Acad Sci, Natl Astron Observ, Key Lab Solar Act, Beijing 100101, Peoples R China
2.Shenzhen Univ, Coll Math & Stat, Shenzhen 518060, Peoples R China
3.Peking Univ, Natl Engn Lab Video Technol, Beijing 100871, Peoples R China
Recommended Citation
GB/T 7714
Xu, Long,Yan, Yi-Hua,Yu, Xue-Xin,et al. LSTM neural network for solar radio spectrum classification[J]. RESEARCH IN ASTRONOMY AND ASTROPHYSICS,2019,19(9):12.
APA Xu, Long,Yan, Yi-Hua,Yu, Xue-Xin,Zhang, Wei-Qiang,Chen, Jie,&Duan, Ling-Yu.(2019).LSTM neural network for solar radio spectrum classification.RESEARCH IN ASTRONOMY AND ASTROPHYSICS,19(9),12.
MLA Xu, Long,et al."LSTM neural network for solar radio spectrum classification".RESEARCH IN ASTRONOMY AND ASTROPHYSICS 19.9(2019):12.
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