本文選題：支持向量機 + 核函數(shù)�。� 參考：《廣東工業(yè)大學》2015年碩士論文

【摘要】：隨著計算機和互聯(lián)網(wǎng)的持續(xù)高速發(fā)展,網(wǎng)絡在人們生活中扮演的角色也越來越重要,人們再也不能滿足于只簡單上網(wǎng)的需求,人們對上網(wǎng)的要求也越來越高。網(wǎng)絡擁塞、網(wǎng)絡故障、網(wǎng)絡安全等一系列的問題時刻困擾著我們,如何對系統(tǒng)中的網(wǎng)絡數(shù)據(jù)進行測量、收集和預測已成為網(wǎng)絡系統(tǒng)運行的主要難題之一。據(jù)大量數(shù)據(jù)顯示,網(wǎng)絡是復雜的、多方因素影響的,網(wǎng)絡流量也必然呈現(xiàn)出高度自相似、時變性和非線性等特征,這注定傳統(tǒng)的預測方法無法做到高的準確率。支持向量機是一種機器學習方法,其求解速度快,且泛化能力強,故本文用支持向量機來進行預測。支持向量機可以根據(jù)現(xiàn)有的有限的樣本信息,在所建立的模型的復雜性和機器的學習能力間尋求一個平衡點,以得到最好的泛化能力,并創(chuàng)造性的將線性不可分的問題,通過核函數(shù)映射到高維空間,使之線性可分。本文在對網(wǎng)絡流量準確預測后,綜合預測了CPU使用率和內存使用率的情況,為市區(qū)信訪件對接平臺設計了模糊控制器,該模糊控制器根據(jù)預測結果進行資源調度,并在仿真平臺上進行了實驗,取得了很好的效果。本文的主要研究內容如下：1).研究支持向量機參數(shù)選擇的問題。參數(shù)的選擇在支持向量機建模期間有巨大的影響,參數(shù)的好壞直接影響著預測精度的高低。在研究生學習期間,本人關注了各種新型的算法,并創(chuàng)新性的將布谷鳥搜索算法應用于支持向量機的參數(shù)選擇過程中。實驗對比了現(xiàn)有的算法,如遺傳算法和粒子群算法,布谷鳥搜索算法明顯提高了SVM的效率和結果準確率。2).根據(jù)記錄的網(wǎng)絡帶寬、CPU使用率,內存使用率的數(shù)據(jù),通過本文提出的基于布谷鳥搜索算法的支持向量回歸機(CS-SVR)進行預測,并通過本文設計的模糊控制器根據(jù)CS-SVR的預測結果,對資源進行調度,使得服務器端的各項資源的利用率最大化,達到負載平衡,從而提高服務質量。
[Abstract]:With the continuous rapid development of computers and the Internet, the role of the network in people's lives is becoming more and more important. People can no longer meet the need of simply accessing the Internet, and people's requirements for the Internet are also getting higher and higher. A series of problems, such as network congestion, network failure, network security and so on, haunt us all the time. How to measure, collect and predict the network data in the system has become one of the main problems in the operation of the network system. According to a large number of data, the network is complex and influenced by many factors, and the network traffic must be highly self-similar, time-varying and nonlinear, which is doomed to the traditional prediction method can not achieve high accuracy. Support vector machine (SVM) is a kind of machine learning method, which has fast solving speed and strong generalization ability, so this paper uses support vector machine to predict. Support vector machine (SVM) can find a balance between the complexity of the established model and the learning ability of the machine based on the existing limited sample information in order to obtain the best generalization ability and creatively solve the problem of linear inseparability. The kernel function is mapped to high dimensional space to make it linearly separable. After the accurate prediction of network traffic, the CPU utilization rate and memory utilization rate are forecasted synthetically, and a fuzzy controller is designed for the docking platform of letters and visits in the urban area. The fuzzy controller schedules the resources according to the forecast results. Experiments are carried out on the simulation platform, and good results are obtained. The main contents of this paper are as follows: 1). The parameter selection of support vector machine (SVM) is studied. The selection of parameters has a great influence on the modeling of support vector machines, and the quality of parameters directly affects the accuracy of prediction. During the post-graduate study, I pay attention to various new algorithms, and creatively apply the cuckoo search algorithm to the parameter selection process of support vector machine. Compared with the existing algorithms, such as genetic algorithm and particle swarm optimization algorithm, the cuckoo search algorithm improves the efficiency and accuracy of SVM significantly. According to the recorded data of CPU utilization and memory utilization, this paper proposes a support vector regression machine (CS-SVR) based on cuckoo search algorithm, and uses the fuzzy controller designed in this paper to predict the CS-SVR. The resources are scheduled to maximize the utilization of each resource on the server side to achieve load balance and improve the quality of service.
【學位授予單位】：廣東工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TP18;TP393.06

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