【摘要】：隨著科技的發(fā)展,機場在信息化數(shù)字化方面也有了長足的進步,機場的各個部門也都開發(fā)或者引進了不同類別的信息管理系統(tǒng),改善了機場信息化應用的環(huán)境。但受制于當前的科技水平、機場各大能源站點復雜的監(jiān)測環(huán)境、電子儀器的不穩(wěn)定性等因素,機場能源數(shù)據(jù)的采集出現(xiàn)各種各樣的困難,采集上來的能源數(shù)據(jù)廣泛存在冗余、缺失、噪聲等不良現(xiàn)象,所以研究新型的機場能源數(shù)據(jù)采集技術,以及研究新型的機場能源數(shù)據(jù)處理算法尤為重要。本論文首先針對機場能源數(shù)據(jù)采集過程中的各種問題,利用無線傳感網(wǎng)絡技術、自動化技術、數(shù)據(jù)庫技術等,設計了機場能源數(shù)據(jù)實時采集平臺,然后對采集到的機場能源數(shù)據(jù)進行處理與分析。根據(jù)機場能源數(shù)據(jù)在不同時間的不同特性提出特征權重的數(shù)據(jù)預處理方法,提出結合經驗模式分解與最小二乘支持向量機的聯(lián)合回歸預測方法解決能源數(shù)據(jù)缺失的問題,同時利用果蠅算法改進最小二乘支持向量機的參數(shù)尋優(yōu)過程。將常用的預測方法與本論文提出的方法進行對比驗證實驗,仿真結果表明采用本論文方法預測準確度有顯著提高,能夠勝任機場能源數(shù)據(jù)缺失的填補工作。最后利用已經建立的回歸預測模型,提出基于無跡變換的機場能源數(shù)據(jù)的改進型卡爾曼濾波方法,在原有卡爾曼濾波方法的基礎上,通過加入誤差反饋提高濾波效果。通過對改進算法的實現(xiàn),得到更加精準的機場能源數(shù)據(jù)。在相同站點條件下進行對比實驗,對比不同濾波方法的濾波效果;在不同站點不同模型的條件下進行驗證實驗,驗證本論文方法的適用性和有效性。結果表明,本論文方法在建立回歸預測模型的基礎上,通過閉環(huán)的誤差反饋控制減少誤差擴散的影響,對于未知的非線性系統(tǒng),有很好的濾波效果,具有良好的發(fā)展與應用前景。
[Abstract]:With the development of science and technology, the airport has made great progress in the field of information digitization. Various departments of the airport have also developed or introduced different kinds of information management systems, which have improved the environment for the application of airport information. However, due to the current level of science and technology, the complex monitoring environment of the major energy stations in the airport, the instability of electronic instruments, and so on, there are various difficulties in the acquisition of airport energy data, and there is widespread redundancy in the energy data collected. Therefore, it is very important to study the new airport energy data acquisition technology and the new airport energy data processing algorithm. In this paper, firstly, aiming at various problems in the process of airport energy data acquisition, using wireless sensor network technology, automation technology, database technology and so on, the real-time acquisition platform of airport energy data is designed. Then the collected airport energy data processing and analysis. According to the different characteristics of airport energy data at different time, the data preprocessing method of feature weight is put forward, and the joint regression prediction method based on empirical mode decomposition and least squares support vector machine is proposed to solve the problem of missing energy data. At the same time, the algorithm of Drosophila was used to improve the parameter optimization process of least squares support vector machine (LS-SVM). The simulation results show that the prediction accuracy of this method is significantly improved and can be used to fill the lack of airport energy data. Finally, an improved Kalman filtering method based on unscented energy data is proposed by using the established regression prediction model. On the basis of the original Kalman filtering method, the filtering effect is improved by adding error feedback. Through the implementation of the improved algorithm, more accurate airport energy data can be obtained. Comparing the filtering effect of different filtering methods under the same site condition, and verifying the applicability and validity of this method under the condition of different stations and different models. The results show that this method can reduce the influence of error diffusion through closed-loop error feedback control on the basis of establishing regression prediction model, and has a good filtering effect for unknown nonlinear systems. It has a good prospect of development and application.
【學位授予單位】：中國民航大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP274.2

【參考文獻】

相關期刊論文 前10條

1 孟yN;;機場能源管理法規(guī)體系的建設研究[J];質量與認證;2015年04期

2 管志威;陳國初;徐余法;俞金壽;;基于改進EMD與SVM的風電功率短期預測模型[J];控制工程;2014年06期

3 田中大;高憲文;李琨;;基于EMD與LS-SVM的網(wǎng)絡控制系統(tǒng)時延預測方法[J];電子學報;2014年05期

4 魏克新;陳峭巖;;基于自適應無跡卡爾曼濾波算法的鋰離子動力電池狀態(tài)估計[J];中國電機工程學報;2014年03期

5 張勇剛;黃玉龍;武哲民;李寧;;一種高階無跡卡爾曼濾波方法[J];自動化學報;2014年05期

6 陳冉;;基于EMD與SVM的短期風速預測研究[J];科技視界;2012年26期

7 肖燕彩;王鵬;韓肖;徐叔陽;;基于EMD與SVM的風電功率短期預測[J];北京交通大學學報;2012年04期

8 張迎春;李t焧，

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