本文關(guān)鍵詞： 短時交通流預測 支持向量回歸 人工魚群算法 參數(shù)選擇 混沌初始化　出處：《大連理工大學》2015年碩士論文　論文類型：學位論文

【摘要】：智能交通系統(tǒng)是緩解道路交通擁堵、減少交通事故和提高交通運行效率的重要應用系統(tǒng)。實時準確可靠的交通流量預測是實現(xiàn)智能交通系統(tǒng)控制和誘導的關(guān)鍵內(nèi)容,具有重大的理論研究和實際應用價值。本文以短時交通流量預測為研究主題,總結(jié)了短時交通流預測的研究現(xiàn)狀,在學習交通流預測原理和支持向量回歸(Support Vector Regression, SVR)理論的基礎上,對基于SVR的短時交通流預測模型中參數(shù)選擇問題進行了探討和研究,運用群智能優(yōu)化方法進行最優(yōu)參數(shù)選擇,并且仿真實際數(shù)據(jù)來驗證提出的預測模型。本文的主要工作如下：1.對人工魚群算法優(yōu)化支持向量回歸的參數(shù)選擇模型進行研究。針對支持向量回歸的懲罰系數(shù)、不敏感損失系數(shù)和核函數(shù)參數(shù)的選擇對回歸算法的預測精度的重要影響,結(jié)合交通流數(shù)據(jù)特征,本文運用人工魚群算法對支持向量回歸參數(shù)進行優(yōu)化選擇,同時引入人工魚群算法中感知視野和移動步長參數(shù)的自適應搜索機制,建立了基于人工魚群算法優(yōu)化支持向量回歸的短時交通流預測模型。實際數(shù)據(jù)的仿真實驗和模型的對比結(jié)果表明了提出的回歸預測模型的可行性和有效性。2.對混合粒子群人工魚群算法優(yōu)化支持向量回歸的參數(shù)選擇模型進行研究。在人工魚群算法優(yōu)化支持向量回歸的預測模型的研究基礎上,為解決人工魚群算法中的初始參數(shù)較多問題以及步長因子設置對尋優(yōu)性能的影響,本文提出采用粒子群優(yōu)化算法對人工魚群算法進行改進,減少了步長因子對人工魚群算法影響,并且引入混沌機制初始化人工魚群位置信息,從而對支持向量回歸進行參數(shù)選擇,建立了基于混合粒子群人工魚群優(yōu)化支持向量回歸的短時交通流預測模型。通過仿真實驗分析,提出的混合優(yōu)化預測模型比單一的粒子群和人工魚群算法優(yōu)化支持向量回歸預測模型有更優(yōu)的預測性能。
[Abstract]:The Intelligent Transportation system (its) is designed to ease traffic congestion on roads. It is an important application system to reduce traffic accidents and improve traffic efficiency. Real time accurate and reliable traffic flow prediction is the key to realize intelligent traffic system control and guidance. It has great theoretical research and practical application value. In this paper, short-term traffic flow forecasting as the research topic, summarized the research status of short-term traffic flow forecasting. On the basis of studying the theory of traffic flow prediction and support vector regression support Vector Regeneration (SVR). The problem of parameter selection in short-term traffic flow forecasting model based on SVR is discussed and studied, and the optimal parameter selection is carried out by using swarm intelligence optimization method. And simulate the actual data to verify the proposed prediction model. The main work of this paper is as follows:. 1. The parameter selection model of support vector regression optimization based on artificial fish swarm algorithm is studied. The penalty coefficient of support vector regression is studied. The selection of insensitive loss coefficient and kernel function parameters has an important influence on the prediction accuracy of regression algorithm. Combined with the characteristics of traffic flow data, the artificial fish swarm algorithm is used to optimize the parameters of support vector regression. At the same time, the adaptive search mechanism of perceptual visual field and moving step size parameters is introduced in artificial fish swarm algorithm. A short-term traffic flow forecasting model based on artificial fish swarm optimization support vector regression is established. The simulation results of the actual data and the comparison of the models show that the proposed regression forecasting model is feasible and effective. The parameter selection model of support vector regression optimization based on hybrid particle swarm optimization algorithm is studied, and the prediction model of support vector regression optimization based on artificial fish swarm algorithm is studied. In order to solve the problem of more initial parameters in artificial fish swarm algorithm and the effect of step size factor setting on the optimization performance, the particle swarm optimization algorithm is proposed to improve the artificial fish swarm algorithm. The influence of step size factor on artificial fish swarm algorithm is reduced, and chaotic mechanism is introduced to initialize the position information of artificial fish swarm, so as to select the parameters of support vector regression. A short-term traffic flow prediction model based on hybrid particle swarm artificial fish swarm optimization support vector regression was established and analyzed by simulation experiments. The proposed hybrid optimization prediction model has better prediction performance than the single particle swarm optimization and artificial fish swarm optimization support vector regression prediction model.
【學位授予單位】：大連理工大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U495;TP18

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本文編號：1490803