本文選題：大壩變形 + 基因表達式編程��； 參考：《江西理工大學(xué)》2014年碩士論文

【摘要】：一直以來，對大壩進行長期的變形監(jiān)測并預(yù)測其變形趨勢，是一項確保大壩安全運行，防止?jié)伟l(fā)生的重要工作。傳統(tǒng)的大壩變形預(yù)測方法或多或少都存在著一些不足，所以研究新的大壩變形預(yù)測方法十分必要�；虮磉_編程算法是新一代獨具優(yōu)勢的遺傳算法，，研究其特性并應(yīng)用于大壩變形預(yù)測中，對促進大壩變形預(yù)測的發(fā)展具有重要意義。 本文首先從大壩變形基本理論出發(fā)，闡述了大壩變形預(yù)測的兩種途徑，即基于影響因子的預(yù)測和基于時間序列的預(yù)測。其次，本文分析了經(jīng)典基因表達式編程算法的基本原理和其在實際應(yīng)用中的不足，并選擇從自適應(yīng)的角度對其進行改進。再次，本文分別研究了基于基因均勻分布的初始種群策略、動態(tài)適應(yīng)度策略和云模型調(diào)整的變異交叉概率策略，并給出了這些改進策略的算法實現(xiàn)。然后，為使改進的基因表達式編程算法更好地應(yīng)用于大壩變形預(yù)測，本文依據(jù)軟件開發(fā)理論和預(yù)測建模的一般流程，從系統(tǒng)的分析設(shè)計和系統(tǒng)的開發(fā)實現(xiàn)兩方面，建立了基于基因表達式編程的大壩變形預(yù)測系統(tǒng)。最后，本文通過一個應(yīng)用實例對改進的基因表達式編程算法的性能和系統(tǒng)的功能進行了測試。 從應(yīng)用實例的結(jié)果看，改進的基因表達式編程算法的預(yù)測精度比經(jīng)典基因表達式編程算法高出一倍左右，且其基于影響因子的預(yù)測精度均在5%以下，這說明了改進后的基因表達式編程算法的整體性能得到了提高。此外，使用本文實現(xiàn)的系統(tǒng)進行大壩的變形預(yù)測，可以依流程執(zhí)行變形預(yù)測的整個過程，也使大壩變形預(yù)測的過程變得更加簡單靈活。
[Abstract]:For a long time, it is an important task to monitor the dam deformation and forecast its deformation trend to ensure the safe operation of the dam and prevent the dam break. Traditional dam deformation prediction methods have some shortcomings, so it is necessary to study new dam deformation prediction methods. Gene expression programming algorithm is a new generation of genetic algorithms with unique advantages. Studying its characteristics and applying it to dam deformation prediction is of great significance to promote the development of dam deformation prediction. In this paper, based on the basic theory of dam deformation, two kinds of methods of dam deformation prediction, namely, the prediction based on influence factors and the prediction based on time series, are expounded in this paper. Secondly, this paper analyzes the basic principle of classical gene expression programming algorithm and its shortcomings in practical application, and chooses to improve it from the point of view of adaptation. Thirdly, the initial population strategy, dynamic fitness strategy and mutation crossover probability strategy based on uniform gene distribution are studied, and the algorithm implementation of these improved strategies is given. Then, in order to better apply the improved genetic expression programming algorithm to dam deformation prediction, according to the software development theory and the general process of prediction modeling, this paper analyzes and implements the system from two aspects: the analysis and design of the system and the development and implementation of the system. A dam deformation prediction system based on gene expression programming is established. Finally, the performance of the improved genetic expression programming algorithm and the function of the system are tested by an application example. The prediction accuracy of the improved genetic expression programming algorithm is about twice as high as that of the classical gene expression programming algorithm, and the prediction accuracy based on the influence factor is less than 5%. This shows that the overall performance of the improved genetic expression programming algorithm has been improved. In addition, the whole process of dam deformation prediction can be carried out by using the system realized in this paper, and the process of dam deformation prediction becomes more simple and flexible.
【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;TP18

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