本文關鍵詞： 機會網絡 深度信念網絡 拓撲預測 相似性指標 最小二乘支持向量回歸機　出處：《南昌航空大學》2016年碩士論文　論文類型：學位論文

【摘要】：機會網絡(Opportunity Network,ON)是一種不需要源節(jié)點和目標節(jié)點之間存在完整連通路徑,利用節(jié)點移動帶來的相遇機會實現通信的移動自組織網絡。機會網絡中節(jié)點移動頻繁,節(jié)點之間間歇性連接,致使其網絡拓撲結構隨時間頻繁地發(fā)生改變,這給機會網絡研究帶來了諸多困難,主要包括路由轉發(fā)機制、網絡負載與效率、網絡服務質量、網絡行為預測等。本文來源國家自然科學基金項目,研究機會網絡中網絡行為預測的拓撲預測問題,主要內容如下:(1)相似性指標的建立;(2)深度信念網絡(Deep Belief Network,DBN)模型的建立;(3)支持向量回歸機的建立。針對機會網絡的時變性,基于時間序列理論和方法,在綜合考慮節(jié)點之間權值、局部路徑和節(jié)點強度三個方面的基礎上,構建了一種能夠反映機會網絡拓撲結構隨時間動態(tài)變化的相似性指標;基于信息熵理論、自適應學習率構建作為特征提取器的DBN模型,其中基于信息熵理論自動計算得到受限玻爾茲曼機(Restricted Boltzmann Machine,RBM)隱含層神經元數量,采用自適應學習率使RBM的重構誤差快速達到平穩(wěn),縮短網絡收斂時間;采用高斯核函數、K折交叉驗證等方法構造基于最小二乘支持向量回歸機(Least Squares Support Vector Regression Machine,LS-SVR)的回歸機模型(DBN-LS-SVR)。本文采用命中率HITR_和受試者工作特征曲線(Receiver Operating Characteristic Curve,ROC)中的Precision、Accuracy指標來評價拓撲預測結果,并且在INFOCOM05(INF’05)、MIT數據集上設計了多組對比實驗驗證DBN-LS-SVR模型。實驗結果表明信息熵方法可以根據輸入數據找出RBM隱含層神經元數量的合適值,自適應學習率可以加快RBM網絡的收斂速度,在一程度上提高了DBN網絡的計算效率;與LS-SVR模型相比,DBN-LS-SVR模型的建模能力和擬合輸入數據的能力更強,能夠獲得更好的預測效果。
[Abstract]:Opportunity Network (ON) is one that does not require a complete connectivity path between the source node and the target node. Mobile ad hoc networks are implemented by using the encounter opportunities brought by node mobility. In opportunistic networks, nodes move frequently and internodes connect intermittently, which results in frequent changes in network topology over time. This brings many difficulties to the research of opportunity network, including routing and forwarding mechanism, network load and efficiency, network quality of service, network behavior prediction and so on. The topology prediction problem of network behavior prediction in opportunistic networks is studied. The main contents are as follows: (1) Establishment of similarity index; (2) Establishment of Deep Belief Network (DBN) model; 3) the establishment of support vector regression machine. Aiming at the time-varying of opportunity network, based on the theory and method of time series, considering the weight between nodes, the local path and the strength of nodes. A similarity index which can reflect the dynamic change of opportunity network topology with time is constructed. Based on the information entropy theory, the adaptive learning rate is used to construct the DBN model as a feature extractor. Based on the information entropy theory, the number of neurons in the hidden layer of restricted Boltzmann machine is calculated automatically. The adaptive learning rate is used to make the reconstruction error of RBM stable quickly, and the convergence time of the network is shortened. Gao Si kernel function is used. K fold cross validation and other methods based on least squares support vector regression (LS-SVM). Least Squares Support Vector Regression Machine. The regression model of LS-SVR is DBN-LS-SVR.The hit ratio HITR_ and the operating characteristic curve of the subjects are used in this paper. Receiver Operating Characteristic Curve. The PrecisionAccuracy indicator in ROC) is used to evaluate the topology prediction results, and is found in INFOCOM05 / INF5). The experimental results show that the information entropy method can find the appropriate number of neurons in the RBM hidden layer according to the input data. Adaptive learning rate can accelerate the convergence speed of RBM network and improve the computational efficiency of DBN network to a certain extent. Compared with LS-SVR model, the model of DBN-LS-SVR has better modeling ability and ability of fitting input data, and can obtain better prediction effect.
【學位授予單位】：南昌航空大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TN929.5

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