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朱正平,阮鵬飛.三亞地區電離層foF2的混沌特性分析及其預測研究[J].中南民族大學學報自然科學版,2019,(2):231-237
三亞地區電離層foF2的混沌特性分析及其預測研究
Analysis of chaotic features and prediction of ionospheric foF2 in Sanya
  
DOI:10.12130/znmdzk.20190216
中文關鍵詞: 電離層foF2  混沌  神經網絡  預測
英文關鍵詞: ionosphere foF2  chaos  neural network  prediction
基金項目:國家自然科學基金資助項目(41474135,41474134)
作者單位
朱正平,阮鵬飛 中南民族大學 電子信息工程學院,武漢 430074 
摘要點擊次數: 186
全文下載次數: 176
中文摘要:
      利用三亞臺站2013年電離層foF2觀測數據,討論了電離層foF2的混沌特性及其預測。采用改進的C-C算法確定時間延遲和嵌入維數,計算最大李雅普諾夫指數,定量地印證foF2時間序列具有混沌特性?;赗BF神經網絡的方法對foF2參量進行短期預報,并將預報結果與Volterra模型、IRI模型和實測數據進行對比。結果表明,采用RBF神經網絡法可成功預測foF2的變化,相比于國際參考電離層模型有較大提高,較Volterra模型也有一定提升。在一定時間尺度內,RBF神經網絡預測結果較為準確,預測誤差較小,超出該預測范圍,預測效果將變差。
英文摘要:
      Chaotic features of critical frequency of the ionospheric F2 layer (foF2) and its prediction are discussed based on the observation data of foF2 in Sanya in 2013. The improved C-C algorithm is adopted to determine the time delay and embedding dimension and the maximum Lyapunov index is calculated to verify the chaotic property of foF2 time series quantitatively. The method based on RBF neural network is used for short-term forecast of foF2 parameters and the forecast results were compared with the Volterra model, the IRI model and the measured ones. It shows that RBF neural network can predict the variation of foF2 successfully. The method of using RBF neural network is superior to IRI model and Volterra model. Predicted results are more accurate and less prediction errors within a certain time scale using RBF neural network. Beyond the range, the effect will be worse.
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