本文關(guān)鍵詞： 無線網(wǎng)絡 流量建模預測 服務質(zhì)量 神經(jīng)網(wǎng)絡 穩(wěn)定小波變換 量子遺傳算法　出處：《北京郵電大學》2014年碩士論文　論文類型：學位論文

【摘要】：無線網(wǎng)絡在信息化社會中扮演著越來越重要的角色。無線網(wǎng)絡能夠輕易、有效的進行高速通信,為人們的生活提供便利的同時,也為國家經(jīng)濟、政治、軍事帶來了新的發(fā)展契機。隨著越來越多的無線寬帶網(wǎng)絡接入點部署在生產(chǎn)生活中,無線網(wǎng)絡規(guī)模日益龐大,環(huán)境日趨復雜,網(wǎng)絡運營商缺乏有效保證無線網(wǎng)絡服務質(zhì)量(QoS)的手段,對于無線網(wǎng)絡流量的模型、特征、可靠性需要進一步研究,使得保障網(wǎng)絡QoS、維護網(wǎng)絡安全、網(wǎng)絡故障檢測等工作難以深入開展,網(wǎng)絡流量的建模預測已經(jīng)成為解決這一問題的主要工具。本文針對無線網(wǎng)絡流量本身和基于優(yōu)化人工神經(jīng)網(wǎng)絡的建模預測進行了系統(tǒng)研究。 為掌握無線網(wǎng)絡流量的預測方法,本文首先對無線網(wǎng)絡流量數(shù)據(jù)進行了研究,通過分析其統(tǒng)計特性、相關(guān)特性、自相似性、混沌特性等,并與有線網(wǎng)絡流量對比,驗證了無線網(wǎng)絡流量具有更強的分散性、突發(fā)性和混沌特性。 接著,本文對時間序列預測方法進行了調(diào)研,分析了傳統(tǒng)時間序列分析、混沌時間序列分析的方法,進一步研究ARIMA模型和混沌RBF神經(jīng)網(wǎng)絡模型的預測方法,發(fā)現(xiàn)這些模型在網(wǎng)絡流量預測中存在一定缺陷,需要更精確的模型來預測無線流量。 然后,本文重點研究BP神經(jīng)網(wǎng)絡、量子遺傳算法和小波變換理論,深入探討B(tài)P神經(jīng)網(wǎng)絡的概念、原理和優(yōu)缺點,分析神經(jīng)網(wǎng)絡優(yōu)化的方法,提出一種利用量子遺傳算法高效的全局搜索能力來優(yōu)化神經(jīng)網(wǎng)絡的方法。在此基礎(chǔ)之上,結(jié)合穩(wěn)定小波變換,利用BP神經(jīng)網(wǎng)絡良好的魯棒性和非線性處理能力,提出一種基于優(yōu)化神經(jīng)網(wǎng)絡的混合無線網(wǎng)絡流量預測模型,命名為SWT-QGA-BP模型。 最后,仿真實驗對無線網(wǎng)絡流量進行單步、多步預測,結(jié)合預測評估指標,對提出的SWT-QGA-BP模型的預測結(jié)果進行評價,對比ARIMA模型和混沌RBF神經(jīng)網(wǎng)絡模型,驗證了新模型的自適應性和預測性能優(yōu)越性。提出的SWT-QGA-BP模型能夠更加準確高效的對無線網(wǎng)絡流量進行預測,有能力為網(wǎng)絡保障QOS、網(wǎng)絡資源管理、網(wǎng)絡安全維護提供必要的助力。
[Abstract]:Wireless network plays a more and more important role in the information society. Wireless network can easily and effectively carry out high-speed communication, provide convenience for people's life, at the same time, it is also good for national economy and politics. As more and more wireless broadband network access points are deployed in production and daily life, the scale of wireless network is becoming larger and larger, and the environment is becoming more and more complex. Network operators lack of effective means to ensure the quality of service (QoS) of wireless networks. The model, characteristics and reliability of wireless network traffic need further study to ensure network QoS and maintain network security. Network fault detection is difficult to carry out in depth, and modeling and forecasting of network traffic has become the main tool to solve this problem. In this paper, wireless network traffic itself and modeling and prediction based on optimized artificial neural network are systematically studied. In order to master the prediction method of wireless network traffic, this paper first studies the wireless network traffic data, through the analysis of its statistical characteristics, correlation characteristics, self-similarity, chaos characteristics, and so on, and compared with the wired network traffic. It is proved that wireless network traffic has more dispersive, sudden and chaotic characteristics. Then, this paper investigates the prediction methods of time series, analyzes the methods of traditional time series analysis and chaotic time series analysis, and further studies the prediction methods of ARIMA model and chaotic RBF neural network model. It is found that these models have some defects in network traffic prediction, and more accurate models are needed to predict wireless traffic. Then, this paper focuses on BP neural network, quantum genetic algorithm and wavelet transform theory, deeply discusses the concept, principle, advantages and disadvantages of BP neural network, and analyzes the optimization method of neural network. This paper presents a method of optimizing neural network by using the high efficient global search ability of quantum genetic algorithm. On this basis, combining with stable wavelet transform, the good robustness and nonlinear processing ability of BP neural network are utilized. A hybrid wireless network traffic prediction model, named SWT-QGA-BP model, is proposed based on optimized neural network. Finally, the simulation experiments are used to predict the wireless network traffic in single step and multi-step. Combined with the prediction evaluation index, the prediction results of the proposed SWT-QGA-BP model are evaluated, and the comparison between the ARIMA model and the chaotic RBF neural network model is carried out. The proposed SWT-QGA-BP model can predict wireless network traffic more accurately and efficiently, and can provide necessary assistance for QoS, network resource management and network security maintenance.
【學位授予單位】：北京郵電大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP18;TP393.06

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本文編號：1498857