本文選題：機場噪聲 + 關(guān)聯(lián)規(guī)則��； 參考：《南京航空航天大學(xué)》2014年碩士論文

【摘要】：近年來隨著民航事業(yè)的迅速發(fā)展，機場噪聲越來越成為人們關(guān)注的話題。借助機場噪聲監(jiān)測點數(shù)據(jù)集挖掘機場噪聲分布情況和發(fā)展趨勢，才能為有效地預(yù)測并采取有效措施防治飛機噪聲污染和有計劃地開發(fā)利用機場及周邊區(qū)域的土地提供重要的科學(xué)依據(jù)。 本文主要研究的是機場噪聲監(jiān)測點噪聲值之間的關(guān)聯(lián)規(guī)則。 關(guān)聯(lián)規(guī)則是目前數(shù)據(jù)挖掘與知識發(fā)現(xiàn)的主要研究內(nèi)容之一，側(cè)重于確定數(shù)據(jù)中不同屬性之間的聯(lián)系，找出滿意約定支持度(Support)和信任度(Confidence)閾值的多個屬性之間的依賴關(guān)系。自1993年R.Agrawal，R.Srikant首次提出該問題來，已經(jīng)出現(xiàn)了許多關(guān)聯(lián)規(guī)則挖掘算法。 本文的工作主要分為以下幾個方面： 1)研究當(dāng)前主要的關(guān)聯(lián)規(guī)則挖掘算法。研究當(dāng)前主要的關(guān)聯(lián)規(guī)則挖掘算法，并分析總結(jié)各種算法存在的優(yōu)勢和不足。 2)研究機場噪聲監(jiān)測點數(shù)據(jù)集的特點。根據(jù)監(jiān)測點數(shù)據(jù)集的特點，本文采用DENCLUE算法對監(jiān)測點數(shù)據(jù)集進(jìn)行聚類，并根據(jù)改進(jìn)的爬山算法找出每一類中的代表點。 3)分析機場噪聲監(jiān)測點數(shù)據(jù)集的影響因素。監(jiān)測點噪聲值主要與天氣、溫濕度、航跡、跑道、機型等信息密切相關(guān)。本文采用了灰色關(guān)聯(lián)度分析法對同一航班不同機型的噪聲影響進(jìn)行分析，并進(jìn)行了實驗驗證。結(jié)果表明，灰色關(guān)聯(lián)度分析方法在對于影響噪聲值條件的選擇具有可行性。 4)建立關(guān)聯(lián)規(guī)則的初始集。首先根據(jù)噪聲值的分貝數(shù)，刪除那些非噪聲事件的噪聲值，然后再根據(jù)航跡，刪除非航跡上的監(jiān)測點的噪聲值，最后采用等分法將噪聲值轉(zhuǎn)變?yōu)樵肼曋祬^(qū)間。 5)挖掘監(jiān)測點噪聲值之間的關(guān)聯(lián)規(guī)則。本文應(yīng)用Apriori算法挖掘關(guān)聯(lián)規(guī)則，并指出了Apriori算法的不足，在此基礎(chǔ)上提出了ATNSOA-Apriori算法，最后將兩者進(jìn)行了實驗對比。結(jié)果表明ATNSOA-Apriori算法更有效率。 6)關(guān)聯(lián)規(guī)則的數(shù)量的回歸分析。利用回歸分析法設(shè)計幾種回歸方程，然后利用復(fù)相關(guān)系數(shù)檢驗各個回歸方程的擬合效果，，最后用顯著性檢驗來驗證參數(shù)的系數(shù)是否顯著為零。本文采用復(fù)相關(guān)系數(shù)最大的回歸方程作為最優(yōu)方程來預(yù)測給定參數(shù)下的關(guān)聯(lián)規(guī)則的數(shù)量。
[Abstract]:In recent years, with the rapid development of civil aviation, airport noise has become a topic of concern. With the aid of the data set of airport noise monitoring points, the noise distribution and development trend of excavator field are analyzed. It can provide an important scientific basis for effectively predicting and taking effective measures to prevent and control aircraft noise pollution and to develop and utilize the land in the airport and its surrounding areas in a planned way. This paper mainly studies the association rules between noise values of airport noise monitoring points. Association rule is one of the main research contents of data mining and knowledge discovery at present. It focuses on determining the relationship between different attributes in data, and finds out the dependencies between several attributes of satisfactory agreement support and confidence degree Confidence. Since R. Agrawaln R. Srikant first proposed this problem in 1993, there have been many association rules mining algorithms. The work of this paper is divided into the following aspects: 1) the main algorithms for mining association rules are studied. The main algorithms for mining association rules are studied, and the advantages and disadvantages of these algorithms are analyzed and summarized. 2) the characteristics of airport noise monitoring data set are studied. According to the characteristics of monitoring data set, this paper uses DENCLUE algorithm to cluster the data set of monitoring points, and finds out the representative points in each class according to the improved mountain climbing algorithm. 3) analyze the influence factors of airport noise monitoring data set. The noise of monitoring points is closely related to weather, temperature and humidity, track, runway, type and so on. In this paper, grey correlation analysis method is used to analyze the noise effect of different types of aircraft on the same flight, and the experimental results are verified. The results show that the grey correlation degree analysis method is feasible for the selection of the conditions affecting the noise value. 4) establishing the initial set of association rules. The noise values of those non-noise events are deleted according to the decibels of the noise values, and then the noise values of the monitoring points on the non-track are deleted according to the track. Finally, the noise values are transformed into the noise value intervals by using the equipartition method. 5) mining association rules between noise values of monitoring points. In this paper, Apriori algorithm is applied to mining association rules, and the deficiency of Apriori algorithm is pointed out. On this basis, the ATNSOA-Apriori algorithm is proposed, and finally, the experimental comparison between the two algorithms is carried out. The results show that ATNSOA-Apriori algorithm is more efficient. 6) regression analysis of the number of association rules. Regression analysis is used to design several regression equations, and then the complex correlation coefficient is used to test the fitting effect of each regression equation. At last, the significance test is used to verify whether the coefficients of the parameters are significantly zero. In this paper, the regression equation with the largest complex correlation coefficient is used as the optimal equation to predict the number of association rules under given parameters.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：V351;TB53

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