本文選題：海水位波動 + 海水位波動預(yù)測　； 參考：《華東師范大學(xué)》2017年碩士論文

【摘要】：隨著全球變暖不斷加劇,海水位的上升問題逐漸引起了人們的關(guān)注。大量研究基于長期海水位的趨勢分析,相比之下,短時海水位波動的研究相對較少。本文實現(xiàn)了對短時海水位時間序列的預(yù)測,并對預(yù)測結(jié)果進行了分析,設(shè)計了海水位預(yù)測系統(tǒng)。本文的主要亮點有:一是使用了線性模型——自回歸模型和非線性模型——支持向量機和BP神經(jīng)網(wǎng)絡(luò)對海水位時間序列進行了預(yù)測。結(jié)果表明,支持向量機和BP神經(jīng)網(wǎng)絡(luò)在樣本長度較小時表現(xiàn)出了比自回歸模型更為精確的預(yù)測結(jié)果,使用非線性模型大大縮短了預(yù)測所需的樣本長度,并在增大預(yù)測步長時依然保持著低于線性模型的預(yù)測誤差。實驗證明,支持向量機和BP神經(jīng)網(wǎng)絡(luò)的應(yīng)用將大大節(jié)約海水位時間序列預(yù)測成本;二是得到了海水位時間序列預(yù)測誤差與預(yù)測步長的關(guān)系曲線,并用冪函數(shù)擬合得到具體的函數(shù)表達式。本文使用了支持向量機、BP神經(jīng)網(wǎng)絡(luò)和自回歸模型分別驗證了預(yù)測誤差與預(yù)測步長的定量關(guān)系,對今后工作中預(yù)測步長的選取和預(yù)測誤差的估計有著借鑒意義;三是利用MALTAB GUI平臺設(shè)計了海水位時間序列預(yù)測系統(tǒng)。該系統(tǒng)具有海水位時間序列的預(yù)測功能,可以計算出分別使用三類預(yù)測模型時的預(yù)測誤差,并自動生成預(yù)測誤差與預(yù)測步長關(guān)系曲線、預(yù)測誤差與樣本長度關(guān)系曲線,產(chǎn)生定量關(guān)系式。用戶使用該系統(tǒng)可以全面了解預(yù)測模型,深入理解海水位時間序列的預(yù)測結(jié)果。
[Abstract]:With the increasing of global warming, the problem of rising seawater level has attracted more and more attention. A large number of studies are based on the trend analysis of long term seawater level. In this paper, the prediction of short-term seawater potential time series is realized, and the prediction results are analyzed, and a seawater level prediction system is designed. The main highlights of this paper are as follows: first, the potential time series of seawater are predicted by using linear model-autoregressive model and nonlinear model-support vector machine (SVM) and BP neural network. The results show that support vector machine and BP neural network show more accurate prediction results than the autoregressive model when the sample length is small, and the nonlinear model can greatly shorten the sample length required for prediction. The prediction error is still lower than that of the linear model when the prediction step is increased. Experimental results show that the application of support vector machine and BP neural network will greatly reduce the prediction cost of seawater potential time series, and the relationship curve between prediction error and prediction step size of seawater potential time series is obtained. The specific function expression is obtained by power function fitting. In this paper, support vector machine BP neural network and autoregressive model are used to verify the quantitative relationship between prediction error and prediction step size, which is useful for the selection of prediction step size and estimation of prediction error in future work. The third is the design of seawater potential time series prediction system based on MALTAB GUI platform. The system has the function of predicting the time series of seawater potential. It can calculate the prediction errors when using three kinds of prediction models, and automatically generate the curve of relation between prediction error and prediction step size, and the relationship curve between prediction error and sample length. A quantitative relationship is produced. With the system, the prediction model can be fully understood and the prediction results of seawater potential time series can be deeply understood.
【學(xué)位授予單位】：華東師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：O211.61

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