lstmneuralnetworkforsolarradiospectrumclassification
文献类型：期刊论文
| 作者 | Xu Long1 ; Yan Yihua1; Yu Xuexin1; Zhang Weiqiang3; Chen Jie2; Duan Lingyu2
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| 刊名 | researchinastronomyandastrophysics
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| 出版日期 | 2019 |
| 卷号 | 19期号:9 |
| ISSN号 | 1674-4527 |
| 英文摘要 | 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. |
| 语种 | 英语 |
| 源URL | [http://ir.bao.ac.cn/handle/114a11/28271]  |
| 专题 | 中国科学院国家天文台 |
| 作者单位 | 1.中国科学院国家天文台 2.北京大学 3.深圳大学 |
| 推荐引用方式 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). |
入库方式： OAI收割
来源：国家天文台
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