本文選題：大壩　切入點(diǎn)：變形預(yù)測　出處：《江西理工大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：為保障大壩的安全運(yùn)營狀態(tài)，需要根據(jù)已有的觀測數(shù)據(jù)預(yù)測大壩未來的變形量。而目前對于大壩變形的預(yù)測方法較多，傳統(tǒng)的預(yù)測方法存在預(yù)測過程復(fù)雜、速度慢、預(yù)測精度低等問題�；虮磉_(dá)式編程是在遺傳算法和遺傳編程的基礎(chǔ)上發(fā)展起來的一種新型演化算法，它被廣泛應(yīng)用在災(zāi)害預(yù)警等預(yù)測領(lǐng)域。因此，非常有必要開展基于基因表達(dá)式編程的大壩變形預(yù)測研究。 本文首先闡述了大壩變形預(yù)測和基因表達(dá)式編程的國內(nèi)外研究現(xiàn)狀，介紹了大壩變形監(jiān)測理論和大壩預(yù)測模型；其次，根據(jù)基因表達(dá)式編程原理及算法流程，確定模型構(gòu)建包括函數(shù)集與終止符集的確定、種群初始化、染色體解碼、適應(yīng)度評價(jià)、遺傳操作等過程，利用Visual Studio平臺下的C#編程語言，，完成了模型的構(gòu)建工作；然后，利用C#語言設(shè)計(jì)Fibonacci加權(quán)滑動窗口法程序，對大壩監(jiān)測數(shù)據(jù)進(jìn)行預(yù)處理；最后，用MATLAB軟件建立灰色GM(1,1)模型和BP神經(jīng)網(wǎng)絡(luò)模型，分別利用基于基因表達(dá)式編程預(yù)測模型、灰色GM(1,1)預(yù)測模型和BP神經(jīng)網(wǎng)絡(luò)模型對某大壩進(jìn)行了變形預(yù)測，并分析了三種預(yù)測模型的預(yù)測結(jié)果。 通過計(jì)算可知，三種模型在水平位移的平均相對誤差分別為1.43%、3.85%和3.08%；在垂直位移的平均相對誤差分別為1.87%、5.54%和4.83%。實(shí)驗(yàn)結(jié)果表明，基因表達(dá)式編程的大壩變形預(yù)測模型精度相對較高，優(yōu)于灰色GM(1,1)模型和BP神經(jīng)網(wǎng)絡(luò)模型的預(yù)測精度。由此可知基于基因表達(dá)式編程的大壩變形預(yù)測模型為大壩變形預(yù)測提供了一種新的方法。
[Abstract]:In order to ensure the safe operation of the dam, it is necessary to predict the future deformation of the dam according to the existing observation data. Genetic expression programming is a new evolutionary algorithm developed on the basis of genetic algorithm and genetic programming. It is widely used in prediction fields such as disaster warning. It is necessary to study dam deformation prediction based on genetic expression programming. In this paper, the research status of dam deformation prediction and gene expression programming is introduced, and the dam deformation monitoring theory and dam prediction model are introduced. The construction of deterministic model includes the determination of function set and terminator set, population initialization, chromosome decoding, fitness evaluation, genetic operation and so on. Using C # programming language based on Visual Studio, the model is constructed. The program of Fibonacci weighted sliding window method is designed by C # language to preprocess the dam monitoring data. Finally, the grey GM1 / 1) model and BP neural network model are established by MATLAB software, and the prediction model based on genetic expression programming is used, respectively. The deformation prediction of a dam is carried out by using the grey GMX1) prediction model and the BP neural network model, and the results of three kinds of prediction models are analyzed. The average relative errors of the three models in horizontal displacement are 1.433.85% and 3.08%, respectively, and the average relative errors in vertical displacement are 1.875.54% and 4.833%, respectively. The precision of dam deformation prediction model based on gene expression programming is relatively high. The prediction accuracy of the model is better than that of the grey GMX1) model and the BP neural network model, which shows that the dam deformation prediction model based on genetic expression programming provides a new method for dam deformation prediction.
【學(xué)位授予單位】：江西理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TV698.11

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