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  本文選題：短時交通流預測　切入點：小波神經(jīng)網(wǎng)絡　出處：《西南交通大學》2017年碩士論文　論文類型：學位論文

【摘要】：在智能交通系統(tǒng)中,實時準確的短時交通流預測一直是各國學者研究的重點。為了提高預測精度,越來越多的組合模型被應用到此領域,其中小波神經(jīng)網(wǎng)絡模型結合了小波分析與神經(jīng)網(wǎng)絡的優(yōu)勢,對短時交通流的預測具有較好的效果,狼群算法(Wolf Pack Algorithm,WPA)是新近提出的優(yōu)化算法,具有較好的全局收斂性,為此,本文基于小波神經(jīng)網(wǎng)絡,將狼群算法與改進的梯度下降算法相結合,用于短時交通流預測。首先從美國明尼蘇達德盧斯大學交通數(shù)據(jù)研究實驗室以及加州運輸性能測量系統(tǒng)中獲得了 5個交通流數(shù)據(jù)集;然后對每個數(shù)據(jù)集的數(shù)據(jù)進行修復、小波降噪、相空間重構和歸一化等預處理,用于所有模型的仿真實驗;最后使用Morlet小波神經(jīng)網(wǎng)絡(Wavelet Neural Network,WNN)對短時交通流進行預測。仿真實驗結果表明,小波神經(jīng)網(wǎng)絡模型能夠?qū)�交通流的整體趨勢進行預測,但穩(wěn)定性和預測精度還有待提高。針對小波神經(jīng)網(wǎng)絡中梯度下降算法對權值和小波因子初值敏感,容易陷入局部極小值的缺點,本文將狼群算法與梯度下降算法結合,先利用狼群算法的全局尋優(yōu)能力為小波神經(jīng)網(wǎng)絡找到一組較優(yōu)的權值和小波因子,再通過梯度下降算法對權值和小波因子尋優(yōu),仿真實驗結果表明狼群算法與梯度下降算法的結合是有效的。在此基礎上對狼群算法進行了改進,仿真實驗結果表明,IWPA-WNN模型有效地提高了短時交通流預測的穩(wěn)定性和精度,同時縮短了運行時間。其次,為了進一步提高短時交通流預測的精度,本文將誤差補償方法(Error Compensation,EC)應用到小波神經(jīng)網(wǎng)絡短時交通流預測中,使用小波神經(jīng)網(wǎng)絡對交通流預測的誤差數(shù)據(jù)進行二次信息提取,仿真實驗結果表明,加入了誤差補償?shù)男〔ㄉ窠?jīng)網(wǎng)絡能夠有效地提高短時交通流預測的精度。最后將誤差補償方法與改進狼群算法的小波神經(jīng)網(wǎng)絡有機結合構成了 EC-IWPA-WNN短時交通流預測模型。仿真實驗結果表明基于EC-IWPA-WNN模型的短時交通流預測在穩(wěn)定性和精度上都具有良好的性能。
[Abstract]:In intelligent transportation system, real-time and accurate short-term traffic flow prediction has been the focus of scholars all over the world. In order to improve the prediction accuracy, more and more combined models have been applied to this field. The wavelet neural network model combines the advantages of wavelet analysis and neural network, and has a good effect on short-term traffic flow prediction. Wolf Pack algorithm is a newly proposed optimization algorithm, which has good global convergence. Based on wavelet neural network, this paper combines the improved gradient descent algorithm with the wolf swarm algorithm. First, five traffic flow data sets were obtained from the Traffic data Research Laboratory of the University of Minnesota, Duluth, and the California Transportation performance Measurement system; then the data of each data set was repaired. The pretreatment of wavelet denoising, phase space reconstruction and normalization is used in simulation experiments of all models. Finally, Morlet wavelet Neural network is used to predict short-term traffic flow. The simulation results show that, The wavelet neural network model can predict the overall trend of traffic flow, but the stability and prediction accuracy need to be improved. The gradient descent algorithm in wavelet neural network is sensitive to the weights and the initial values of wavelet factors. It is easy to fall into local minima. In this paper, the wolf swarm algorithm is combined with the gradient descent algorithm. Firstly, the global optimization ability of the wolf swarm algorithm is used to find a set of better weights and wavelet factors for the wavelet neural network. Then the weight and wavelet factor are optimized by gradient descent algorithm. The simulation results show that the combination of wolf swarm algorithm and gradient descent algorithm is effective. The simulation results show that the IWPA-WNN model can effectively improve the stability and accuracy of short-term traffic flow prediction and shorten the running time. Secondly, in order to further improve the accuracy of short-term traffic flow prediction, In this paper, the error compensation method is applied to short-term traffic flow prediction based on wavelet neural network. The error data of traffic flow prediction is extracted by wavelet neural network. The simulation results show that, Wavelet neural network with error compensation can effectively improve the accuracy of short-term traffic flow prediction. Finally, combining the error compensation method with the wavelet neural network of improved wolf swarm algorithm, EC-IWPA-WNN short-time traffic flow prediction is formed. The simulation results show that the short-time traffic flow prediction based on EC-IWPA-WNN model has good stability and accuracy.
【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】：U491.14;TP18

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