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

【摘要】：太陽(yáng)耀斑是最劇烈的太陽(yáng)活動(dòng)。耀斑爆發(fā)時(shí)，太陽(yáng)噴射出的大量的高能粒子到達(dá)地球附近時(shí)，能夠影響到在地球軌道區(qū)域正常運(yùn)行的人造衛(wèi)星；同時(shí)國(guó)際空間站的宇航員的人身生命也面臨著危險(xiǎn)。耀斑爆發(fā)所釋放出的X射線及其紫外射線，到達(dá)地球大氣層中的電離層，能夠破壞電磁通訊；無(wú)線電通信特別是短波通信，如手機(jī)通話、電臺(tái)廣播，，等會(huì)受到干擾甚至中斷。 采取有效的方法研究和預(yù)測(cè)太陽(yáng)耀斑的級(jí)別，對(duì)于減少和避免太陽(yáng)耀斑爆發(fā)給人民帶來(lái)的財(cái)產(chǎn)和生命上損失具有很重要的意義。由于太陽(yáng)活動(dòng)的復(fù)雜與頻繁，太陽(yáng)耀斑的爆發(fā)與諸多因素有關(guān)，而這諸多因素又與太陽(yáng)耀斑級(jí)別的預(yù)測(cè)有著復(fù)雜的非線性關(guān)系。采用具有較強(qiáng)的非線性逼近能力的人工神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)太陽(yáng)耀斑級(jí)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(luò)算法進(jìn)行太陽(yáng)耀斑級(jí)別的預(yù)測(cè)。 由于BP神經(jīng)網(wǎng)絡(luò)模型的實(shí)現(xiàn)需要借助于計(jì)算機(jī)編程語(yǔ)言，實(shí)現(xiàn)起來(lái)比較困難，而MATLAB軟件工具箱功能強(qiáng)大可以解決這一問(wèn)題。本文通過(guò)調(diào)用MATLAB的神經(jīng)網(wǎng)絡(luò)工具箱建立了太陽(yáng)耀斑級(jí)別預(yù)測(cè)的BP神經(jīng)網(wǎng)絡(luò)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(jí)別之間的聯(lián)系。并通過(guò)對(duì)新鄉(xiāng)市在1998年一月到六月期間觀測(cè)記錄的Alpha甚低頻(VLF)導(dǎo)航系統(tǒng)信號(hào)傳播發(fā)生異常時(shí)的65組數(shù)據(jù)進(jìn)行訓(xùn)練仿真，并進(jìn)行預(yù)測(cè)檢驗(yàn)。實(shí)驗(yàn)結(jié)果證明了該模型用于耀斑級(jí)別預(yù)測(cè)的有效性，具有很好的應(yīng)用價(jià)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(xué)位授予單位】：河南師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：P182.52;TP183

【參考文獻(xiàn)】

相關(guān)期刊論文 前8條

1 劉旭春;張正祿;楊軍;;利用GPS監(jiān)測(cè)兩起太陽(yáng)耀斑事件的結(jié)果分析[J];測(cè)繪科學(xué);2007年05期

2 趙協(xié)中;王小京;;太陽(yáng)耀斑期間X射線流量與VLF傳播相位偏移相關(guān)性研究[J];電波科學(xué)學(xué)報(bào);1990年02期

3 牛有田;陳建峰;郝好貞;王海波;張中遠(yuǎn);;1997-03-09日全食的甚低頻觀測(cè)與分析[J];河南師范大學(xué)學(xué)報(bào)(自然科學(xué)版);2009年02期

4 劉萬(wàn)通;VLF相位突然異常與太陽(yáng)X射線事件相關(guān)性的分析[J];空間科學(xué)學(xué)報(bào);1987年03期

5 郭策;薛炳森;林兆祥;;太陽(yáng)X射線耀斑特征參數(shù)預(yù)報(bào)方法研究[J];空間科學(xué)學(xué)報(bào);2012年06期

6 張世田;陳林如;王元新;;2009年7月22日日食期間VLF信號(hào)相位和場(chǎng)強(qiáng)的觀測(cè)和分析[J];時(shí)間頻率學(xué)報(bào);2011年01期

7 王素琴;潘(钅東)德;麻蘭香;;太陽(yáng)X射線爆發(fā)與甚低頻傳播相位異常關(guān)系的研究[J];陜西天文臺(tái)臺(tái)刊;1988年Z1期

8 蔡昌盛,李征航,趙曉峰;太陽(yáng)耀斑的GPS監(jiān)測(cè)方法及實(shí)例分析[J];武漢大學(xué)學(xué)報(bào)(信息科學(xué)版);2003年04期

本文編號(hào)：2094957