本文選題：汽車故障件 + 預測　； 參考：《西南交通大學》2017年碩士論文

【摘要】：近年來,隨著汽車企業(yè)的快速發(fā)展,該行業(yè)數(shù)據(jù)量激增,汽車企業(yè)迫切需要在數(shù)據(jù)中去尋找規(guī)律,根據(jù)歷史數(shù)據(jù)預測配件需求數(shù)量、診斷汽車故障等。在這樣的背景下,許多人工智能算法開始被應用于汽車行業(yè),其中,運用最多的是SVM和BP神經(jīng)網(wǎng)絡(luò)。鑒于BP神經(jīng)網(wǎng)絡(luò)和SVM自身的一些局限性和缺點,本文采用BA-RVM優(yōu)化后的BA-FRVM算法作為汽車典型故障件數(shù)量的預測。本文建立一種基于蝙蝠算法快速相關(guān)向量機的汽車典型故障件數(shù)量預測模型,在Matlab R2014a軟件上實現(xiàn)全部仿真實驗。首先,研究干擾相關(guān)向量機預測準確率的影響因子-核參數(shù),通過蝙蝠算法選擇適合當前數(shù)據(jù)的核參數(shù),達到核參數(shù)的自適應;在汽車典型故障件數(shù)據(jù)下驗證BA-RVM算法,通過仿真實驗選擇合適的特征歸一化方法;其次,通過BA-RVM算法在不同的數(shù)據(jù)下實驗,選出合適的核函數(shù),接著對BA-RVM算法訓練效率進行優(yōu)化,得到BA-FRVM算法,將UCI網(wǎng)站上三種不同類型的數(shù)據(jù)作為實驗數(shù)據(jù),以此來驗證BA-FRVM算法的可信性和可靠性,然后再在汽車典型故障件數(shù)據(jù)下進行實驗,并與BA-RVM算法對比訓練時間、相關(guān)向量數(shù)以及錯誤率;再次,對BA-FRVM算法進行多方面的研究,研究不同迭代次數(shù)與錯誤率的關(guān)系,不同蝙蝠數(shù)量對預測準確率的影響,訓練模型錯誤率與核參數(shù)寬度的關(guān)系,不同訓練樣本量對預測準確率的影響,與相似算法BA-SVR對比訓練時間、相關(guān)(支持)向量數(shù)以及錯誤率,與廣泛應用的BA-BP算法對比訓練時間和錯誤率。最后,將BA-FRVM算法用Java編程語言實現(xiàn),用jblas矩陣庫實現(xiàn)矩陣運算,并將BA-FRVM算法試用于實際的故障件數(shù)量預測系統(tǒng)中。最終,通過實驗結(jié)果可知:相比BA-SVR和BA-BP算法,BA-FRVM算法訓練速度更快,預測準確率更高,能夠更好的適用于汽車典型故障件數(shù)量預測。
[Abstract]:In recent years, with the rapid development of automobile enterprises, the volume of data in this industry has increased rapidly. Automobile enterprises urgently need to find the rules in the data, predict the number of spare parts according to historical data, diagnose automobile faults and so on. In this context, many artificial intelligence algorithms are beginning to be applied to the automotive industry, among which, the most widely used are SVM and BP neural networks. In view of the limitations and shortcomings of BP neural network and SVM, this paper uses the optimized BA-FRVM algorithm of BA-RVM as the prediction of the number of vehicle typical fault parts. In this paper, a fast correlation vector machine based on bat algorithm is established to predict the number of typical fault parts. The simulation experiments are implemented on Matlab R2014a software. Firstly, the kernel parameters, which affect the prediction accuracy of interference correlation vector machines, are studied, the kernel parameters suitable for current data are selected by bat algorithm, and the adaptive kernel parameters are achieved. The BA-RVM algorithm is verified under the vehicle typical fault data. The proper feature normalization method is selected through the simulation experiment. Secondly, the appropriate kernel function is selected through the experiment of BA-RVM algorithm under different data, then the training efficiency of BA-RVM algorithm is optimized, and the BA-FRVM algorithm is obtained. Three different types of data on the UCI website are taken as experimental data to verify the credibility and reliability of the BA-FRVM algorithm, and then the experiment is carried out under the typical fault data of the vehicle, and the training time is compared with that of the BA-RVM algorithm. Thirdly, the relationship between different iterations and error rates, the effects of different bat numbers on prediction accuracy, the relationship between the training model error rate and the width of kernel parameters, and the relationship between the training model error rate and the kernel parameter width are studied. The effect of different training samples on prediction accuracy is compared with similar algorithm BA-SVR, correlation (support) vector number and error rate, training time and error rate compared with widely used BA-BP algorithm. Finally, BA-FRVM algorithm is realized by Java programming language, matrix operation is realized by jblas matrix library, and BA-FRVM algorithm is used in actual fault prediction system. Finally, the experimental results show that: compared with BA-SVR and BA-BP algorithm BA-FRVM algorithm training speed is faster, prediction accuracy is higher, can be better applied to the number of vehicle typical fault prediction.
【學位授予單位】：西南交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U472;TP18

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