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  本文選題：證據(jù)理論 + 評估模型��； 參考：《南京航空航天大學》2014年碩士論文

【摘要】：隨著機場運輸規(guī)模不斷擴大，機場噪聲污染日益惡化，安裝機場噪聲監(jiān)測系統(tǒng)已成為眾多機場監(jiān)測周邊噪聲環(huán)境的重要手段。但是，這類監(jiān)測系統(tǒng)中的固定監(jiān)測點成本高、環(huán)境要求高、穩(wěn)定性差，加之機場噪聲的監(jiān)測數(shù)據(jù)中夾雜有航空器之外的其他噪聲源產生的環(huán)境噪聲（如風噪、施工噪聲等）數(shù)據(jù)，因此評估各監(jiān)測點所監(jiān)測到的噪聲數(shù)據(jù)的可信度就變得尤為重要。 本文在充分分析機場噪聲監(jiān)測數(shù)據(jù)特點的基礎上，提出一種基于證據(jù)理論的機場噪聲監(jiān)測數(shù)據(jù)可信度評估模型。該模型利用數(shù)據(jù)挖掘的方法生成監(jiān)測點間噪聲數(shù)據(jù)的關聯(lián)規(guī)則，然后利用關聯(lián)規(guī)則進行基本概率賦值函數(shù)的獲取，將獲得的證據(jù)用Dempster組合規(guī)則進行融合并作出最終的決策。 針對Dempster組合規(guī)則在沖突證據(jù)融合方面的缺陷，在充分研究了沖突證據(jù)度量標準的基礎上，本文提出使用pignistic概率函數(shù)來衡量證據(jù)之間的沖突程度，，并在此基礎上構建了偽證據(jù)識別方法和沖突證據(jù)融合方法。將這些方法應用于機場噪聲監(jiān)測數(shù)據(jù)可信度評估模型中，使該模型在有沖突證據(jù)的情況下不但能識別出沖突證據(jù)，還能利用改進的合成方法得到正確的融合結果。 實驗表明本文提出的偽證據(jù)識別方法和沖突證據(jù)融合方法能有效地在機場噪聲監(jiān)測數(shù)據(jù)中得到較好的效果，同時改進的評估模型應用在真實的機場噪聲監(jiān)測數(shù)據(jù)中具有較好的準確性和實用性。
[Abstract]:With the expansion of airport transportation scale and the worsening of airport noise pollution, the installation of airport noise monitoring system has become an important means to monitor the surrounding noise environment of many airports. However, the fixed monitoring points in this type of monitoring system have high cost, high environmental requirements, poor stability and environmental noise (such as wind noise) caused by noise sources other than aircraft in the monitoring data of airport noise. Therefore, it is very important to evaluate the reliability of the noise data from the monitoring points. Based on the analysis of the characteristics of airport noise monitoring data, a reliability evaluation model of airport noise monitoring data based on evidence theory is proposed in this paper. The model uses the method of data mining to generate the association rules of noise data between monitoring points, and then uses the association rules to obtain the basic probability assignment function. The obtained evidence is fused with the Dempster combination rule and the final decision is made. Aiming at the defect of Dempster combination rule in conflict evidence fusion, this paper proposes to use pignistic probability function to measure the conflict degree of evidence. On this basis, the pseudo-evidence recognition method and conflict evidence fusion method are constructed. These methods are applied to the reliability evaluation model of airport noise monitoring data. The model can not only identify the conflicting evidence but also obtain the correct fusion results by using the improved synthesis method. The experimental results show that the methods of pseudo-evidence recognition and conflict evidence fusion proposed in this paper can effectively obtain good results in airport noise monitoring data. At the same time, the improved evaluation model has good accuracy and practicability in real airport noise monitoring data.
【學位授予單位】：南京航空航天大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：X839.1;TB53

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