本文選題：變形預(yù)測　切入點(diǎn)：高斯過程　出處：《東華理工大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著經(jīng)濟(jì)的快速發(fā)展，工程建設(shè)也日益興起。工程建筑物的興建，從施工開始到竣工，以及建成后整個(gè)運(yùn)營期間都要不斷地監(jiān)測，以便掌握變形的情況，及時(shí)發(fā)現(xiàn)問題，保證工程建筑的安全。因此，對(duì)大型建筑物進(jìn)行變形監(jiān)測并且對(duì)其數(shù)據(jù)進(jìn)行處理就尤為重要。 目前，國內(nèi)外研究變形分析模型算法有很多，尤其是以神經(jīng)網(wǎng)絡(luò)的智能算法模型。近些年來，“核學(xué)習(xí)”是機(jī)器學(xué)習(xí)領(lǐng)域的熱點(diǎn)問題，其最具代表性的是支持向量機(jī)、高斯過程。高斯過程，作為新興的機(jī)器學(xué)習(xí)方法，提供了一個(gè)原則性的、實(shí)用性的、概率的核機(jī)器學(xué)習(xí)方法。它給預(yù)測模型提供解釋，并提供了模型的選擇和學(xué)習(xí)框架結(jié)構(gòu)。不斷發(fā)展的理論與實(shí)踐的應(yīng)用使高斯過程成為近年來監(jiān)督學(xué)習(xí)應(yīng)用中強(qiáng)有力的競爭者，并在各個(gè)領(lǐng)域有著廣泛的應(yīng)用。本文以該角度為出發(fā)點(diǎn)，研究高斯過程在變形監(jiān)測數(shù)據(jù)處理中的運(yùn)用，主要研究內(nèi)容和成果如下： 1)首先系統(tǒng)闡述高斯過程理論、原理和思路，運(yùn)用高斯過程理論對(duì)變形監(jiān)測數(shù)據(jù)進(jìn)行分析，，通過實(shí)例表明高斯過程回歸在變形監(jiān)測的數(shù)據(jù)處理方面精度高，程序簡單。 2)高斯過程模型中的超參數(shù)主要是由傳統(tǒng)的優(yōu)化方法(共軛梯度法)獲得，但共軛梯度法在優(yōu)化過程中存在依賴初始值、迭代次數(shù)難以確定以及局部優(yōu)化等弊端。針對(duì)傳統(tǒng)方法存在的缺陷，運(yùn)用粒子群算法與高斯過程融合，建立粒子群算法高斯過程隧道位移模型。 3)通過某隧道工程進(jìn)行實(shí)例分析，對(duì)變形監(jiān)測數(shù)據(jù)分別采用高斯過程模型、粒子群高斯過程模型，BP模型進(jìn)行處理。通過一定的誤差指標(biāo)評(píng)價(jià)模型的精度，得到粒子群算法高斯過程模型處理結(jié)果好，有一定的適用性。
[Abstract]:With the rapid development of the economy, the construction of engineering is also rising day by day. The construction of engineering buildings should be continuously monitored from the beginning of construction to the completion of construction, as well as throughout the period of operation after completion, in order to grasp the deformation situation and find problems in time. Therefore, it is very important to monitor the deformation of large buildings and process their data. At present, there are a lot of deformation analysis model algorithms at home and abroad, especially the intelligent algorithm model based on neural network. In recent years, "kernel learning" is a hot issue in the field of machine learning, the most representative of which is support vector machine (SVM). Gao Si process. Gao Si process, as an emerging machine learning method, provides a principled, practical, probabilistic approach to nuclear machine learning. It provides an explanation for the prediction model. It also provides the choice of model and the structure of learning framework. With the application of theory and practice, Gao Si process has become a strong competitor in the application of supervisory learning in recent years. And has been widely used in various fields. This paper studies the application of Gao Si process in deformation monitoring data processing from this point of view. The main research contents and results are as follows:. 1) firstly, Gao Si's process theory, principle and train of thought are expounded systematically, and then the deformation monitoring data are analyzed by the use of Gao Si process theory. It is shown by an example that Gao Si process regression has high precision and simple procedure in the data processing of deformation monitoring. 2) the superparameters in Gao Si's process model are mainly obtained by the traditional optimization method (conjugate gradient method), but the conjugate gradient method depends on the initial value in the optimization process. It is difficult to determine the number of iterations and local optimization. Aiming at the defects of the traditional methods, the particle swarm optimization algorithm is combined with Gao Si process to establish the tunneling displacement model of the particle swarm optimization algorithm. 3) through the analysis of a tunnel project, the deformation monitoring data are processed by Gao Si process model and particle swarm Gao Si process model and BP model respectively. The accuracy of the model is evaluated by certain error indexes. The result of processing Gao Si process model of particle swarm optimization algorithm is good and has certain applicability.
【學(xué)位授予單位】：東華理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：P227

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