【摘要】：在水利工程中有很多高聳或長(zhǎng)大結(jié)構(gòu),如高壩及其壩頂建筑、工作橋及大跨渡槽等,而風(fēng)和地震等時(shí)間序列荷載對(duì)這些結(jié)構(gòu)影響的很大,一些工程因之而受到影響而出現(xiàn)事故,甚至造成災(zāi)難。深入研究風(fēng)與地震等時(shí)間序列荷載的特性和規(guī)律,并對(duì)其進(jìn)行預(yù)測(cè),將有助于結(jié)構(gòu)的振動(dòng)控制,避免或減輕工程災(zāi)害。本文應(yīng)用經(jīng)驗(yàn)?zāi)B(tài)分解及支持向量機(jī)模型,研究風(fēng)速及地震加速度的預(yù)測(cè)問題,主要內(nèi)容如下:1)分析了風(fēng)、地震所具有的不穩(wěn)定的特點(diǎn)以及應(yīng)用于預(yù)測(cè)非平穩(wěn)數(shù)據(jù)序列的方法,對(duì)傳統(tǒng)預(yù)測(cè)方法的特點(diǎn)和目前應(yīng)用較多的預(yù)測(cè)方法進(jìn)行了比較。2)介紹了基于統(tǒng)計(jì)學(xué)理論的支持向量機(jī)和基于結(jié)構(gòu)風(fēng)險(xiǎn)最小化的最小二乘支持向量機(jī)學(xué)習(xí)方法的原理;經(jīng)驗(yàn)?zāi)B(tài)分解方法的基本原理、詳細(xì)分解步驟及一些問題的解決方法;為了提高模型預(yù)測(cè)準(zhǔn)確性,引入粒子群優(yōu)化算法,建立預(yù)測(cè)模型時(shí)模型核函數(shù)和參數(shù)選擇問題、具體的模型建立步驟。最后以風(fēng)速預(yù)測(cè)和地震加速度預(yù)測(cè)為例驗(yàn)證這種預(yù)測(cè)方法的有效性。3)提出了基于經(jīng)驗(yàn)?zāi)B(tài)的分解與多步預(yù)測(cè)的最小二乘支持向量機(jī)相結(jié)合的方法,對(duì)風(fēng)速的非線性時(shí)間序列分析進(jìn)行了建模預(yù)測(cè)。首先對(duì)風(fēng)速動(dòng)態(tài)信號(hào)加以經(jīng)驗(yàn)?zāi)Ｊ降姆纸?將原信號(hào)分解為若干個(gè)不同特征尺度(頻率)的本征模態(tài)函數(shù)。再建立多步預(yù)測(cè)為基礎(chǔ)的最小二乘支持向量機(jī)預(yù)測(cè)模型,對(duì)不同頻帶的平穩(wěn)IMF分量進(jìn)行預(yù)測(cè),將各分量的預(yù)測(cè)值等權(quán)求和得到最終預(yù)測(cè)值。發(fā)現(xiàn)EMD與多步預(yù)測(cè)的LS-SVM相結(jié)合的風(fēng)速預(yù)測(cè)方法比單一的LS-SVM預(yù)測(cè)方法的預(yù)測(cè)精度更高。在此基礎(chǔ)上應(yīng)用PSO優(yōu)化算法對(duì)模型參數(shù)的選擇進(jìn)行優(yōu)化,得到優(yōu)化后的預(yù)測(cè)結(jié)果。4)因?yàn)榈卣鸺铀俣韧瑯泳哂蟹瞧椒�(wěn)的時(shí)間序列的特點(diǎn),所以,同樣地應(yīng)用基于PSO優(yōu)化的EMD-LS-SVM預(yù)測(cè)方法對(duì)加速度序列進(jìn)行建模預(yù)測(cè)。計(jì)算結(jié)果驗(yàn)證了這種組合方法在非平穩(wěn)時(shí)間序列預(yù)測(cè)上的適用性。最后對(duì)本論文中的成果進(jìn)行了分析總結(jié),對(duì)今后的研究方向進(jìn)行了展望。
[Abstract]:In water conservancy projects, there are many tall or long structures, such as high dams and their top buildings, working bridges and long span aqueducts. However, time series loads such as wind and earthquake have a great impact on these structures, and some projects are affected by accidents as a result. Or even disaster. Further study on the characteristics and rules of time series loads such as wind and earthquake, and prediction of them will be helpful to the vibration control of structures and the avoidance or mitigation of engineering disasters. In this paper, empirical mode decomposition and support vector machine model are used to study the prediction of wind speed and earthquake acceleration. The main contents are as follows: 1) the unstable characteristics of wind and earthquake and the methods used to predict non-stationary data series are analyzed. In this paper, the characteristics of traditional prediction methods are compared with the existing prediction methods. (2) the principles of support vector machine based on statistical theory and least-squares support vector machine learning method based on structural risk minimization are introduced. In order to improve the accuracy of model prediction, particle swarm optimization algorithm is introduced to establish the model kernel function and parameter selection. Concrete modeling steps. Finally, wind speed prediction and earthquake acceleration prediction are taken as examples to verify the effectiveness of this prediction method. 3) an empirical modal decomposition method combined with multistep prediction least squares support vector machine is proposed. The nonlinear time series analysis of wind speed is modeled and predicted. Firstly, the wind speed dynamic signal is decomposed by empirical mode, and the original signal is decomposed into several eigenmode functions of different characteristic scales (frequencies). The prediction model of least squares support vector machine (LS-SVM) based on multistep prediction is established. The stationary IMF components of different frequency bands are predicted and the final prediction values are obtained by equal-weight summation of the predicted values of each component. It is found that the prediction accuracy of the wind speed prediction method based on the combination of EMD and multistep LS-SVM is higher than that of the single LS-SVM forecasting method. On this basis, the PSO optimization algorithm is applied to optimize the selection of model parameters, and the optimized prediction result is obtained. (4) because seismic acceleration also has the characteristics of non-stationary time series, so, Similarly, the EMD-LS-SVM prediction method based on PSO optimization is used to model and predict the acceleration sequence. The calculation results verify the applicability of the combined method in the prediction of non-stationary time series. Finally, the results of this paper are analyzed and summarized, and the future research direction is prospected.
【學(xué)位授予單位】：河北農(nóng)業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TV312

【共引文獻(xiàn)】

中國(guó)博士學(xué)位論文全文數(shù)據(jù)庫(kù) 前10條

1 王敏;分布式電源的概率建模及其對(duì)電力系統(tǒng)的影響[D];合肥工業(yè)大學(xué);2010年

2 張穎媛;微網(wǎng)系統(tǒng)的運(yùn)行優(yōu)化與能量管理研究[D];合肥工業(yè)大學(xué);2011年

3 李智;風(fēng)電規(guī)律預(yù)測(cè)及對(duì)電網(wǎng)運(yùn)行影響的研究[D];山東大學(xué);2011年

4 劉輝;鐵路沿線風(fēng)信號(hào)智能預(yù)測(cè)算法研究[D];中南大學(xué);2011年

5 劉偉;綜放工作面煤矸界面識(shí)別理論與方法研究[D];中國(guó)礦業(yè)大學(xué)（北京）;2011年

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7 陳建國(guó);基于獨(dú)立分量分析的機(jī)械故障特征提取及分類方法研究[D];大連理工大學(xué);2011年

8 吳震宇;內(nèi)燃機(jī)故障診斷若干理論與相關(guān)技術(shù)的研究[D];東北大學(xué);2010年

9 高陽(yáng);風(fēng)電場(chǎng)風(fēng)電功率預(yù)測(cè)方法研究[D];沈陽(yáng)農(nóng)業(yè)大學(xué);2011年

10 王揚(yáng);風(fēng)電短期預(yù)測(cè)及其并網(wǎng)調(diào)度方法研究[D];浙江大學(xué);2011年

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本文編號(hào)：2233190