本文選題：太陽耀斑 + MATLB工具箱�。� 參考：《河南師范大學》2014年碩士論文

【摘要】：太陽耀斑是最劇烈的太陽活動。耀斑爆發(fā)時，太陽噴射出的大量的高能粒子到達地球附近時，能夠影響到在地球軌道區(qū)域正常運行的人造衛(wèi)星；同時國際空間站的宇航員的人身生命也面臨著危險。耀斑爆發(fā)所釋放出的X射線及其紫外射線，到達地球大氣層中的電離層，能夠破壞電磁通訊；無線電通信特別是短波通信，如手機通話、電臺廣播，，等會受到干擾甚至中斷。 采取有效的方法研究和預測太陽耀斑的級別，對于減少和避免太陽耀斑爆發(fā)給人民帶來的財產(chǎn)和生命上損失具有很重要的意義。由于太陽活動的復雜與頻繁，太陽耀斑的爆發(fā)與諸多因素有關(guān)，而這諸多因素又與太陽耀斑級別的預測有著復雜的非線性關(guān)系。采用具有較強的非線性逼近能力的人工神經(jīng)網(wǎng)絡預測太陽耀斑級別，能夠綜合考慮到各方面的因素，具用客觀性和有效性。由此本文提出了采用BP神經(jīng)網(wǎng)絡算法進行太陽耀斑級別的預測。 由于BP神經(jīng)網(wǎng)絡模型的實現(xiàn)需要借助于計算機編程語言，實現(xiàn)起來比較困難，而MATLAB軟件工具箱功能強大可以解決這一問題。本文通過調(diào)用MATLAB的神經(jīng)網(wǎng)絡工具箱建立了太陽耀斑級別預測的BP神經(jīng)網(wǎng)絡模型，確定了VLF在電離層中發(fā)生SPA事件的影響因素與耀斑爆發(fā)級別之間的聯(lián)系。并通過對新鄉(xiāng)市在1998年一月到六月期間觀測記錄的Alpha甚低頻(VLF)導航系統(tǒng)信號傳播發(fā)生異常時的65組數(shù)據(jù)進行訓練仿真，并進行預測檢驗。實驗結(jié)果證明了該模型用于耀斑級別預測的有效性，具有很好的應用價值。
[Abstract]:Solar flares are the most intense solar activity. When the flares erupt, a large number of high-energy particles ejected by the sun can affect the normal operation of artificial satellites in the Earth orbit area when they arrive near the earth, and the life of astronauts on the International Space Station is also in danger. The emission of X-ray and ultraviolet rays from flares, reaching the ionosphere in the Earth's atmosphere, can destroy electromagnetic communications; radio communications, especially shortwave communications, such as cell phone calls, radio broadcasts, etc., can be interfered with or even interrupted. It is of great significance to study and predict the level of solar flares in order to reduce and avoid the loss of property and life caused by solar flares. Because of the complexity and frequency of solar activity, the eruption of solar flares is related to many factors, and these factors have a complex nonlinear relationship with the prediction of solar flares. The use of artificial neural networks with strong nonlinear approximation ability to predict solar flares can comprehensively take into account various factors and is objective and effective. In this paper, BP neural network algorithm is used to predict solar flares. Because the realization of BP neural network model needs to be realized by computer programming language, it is difficult to realize it, but MATLAB software toolbox can solve this problem. In this paper, the BP neural network model for predicting solar flare level is established by using the neural network toolbox of MATLAB, and the relationship between the influence factors of SPA event in the ionosphere of VLF and the flare burst level is determined. The 65 sets of data of Alpha very low frequency (Alpha) navigation system which were observed from January to June 1998 were trained and tested. The experimental results show that the model is effective in predicting flares and has good application value.
【學位授予單位】：河南師范大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：P182.52;TP183

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