博碩士論文 etd-0211103-144433 詳細資訊


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姓名 侯俊良(Chun-Liang Hou) 電子郵件信箱 hjl@water.ee.nsysu.edu.tw
畢業系所 電機工程學系研究所(Electrical Engineering)
畢業學位 博士(Ph.D.) 畢業時期 91學年第1學期
論文名稱(中) 類神經網路及其在ATM交通流量控制上之應用
論文名稱(英) Neural Networks and Their Application to Traffic Control in ATM Networks
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    摘要(中)   ATM (Asynchronous transfer mode) 非同步傳輸通訊網路已被公認為多媒體傳輸中最佳的選擇,由於它能夠支援不同性質的流量及不同程度的服務品質需求,因此已逐漸取代傳統的傳輸方式。ATM為了達到品質保證必須有一些流量控制的功能,例如呼叫允許控制及壅塞控制等等。由於傳統方法無法有效地估算出需求頻寬及細胞遺失率,為了改善這些缺點,我們加入了人工智慧的技巧去做流量控制及細胞遺失率的預估,使得ATM控制器更有效率。本論文共分成兩大主題:第一個主題是以類神經網路為基礎的呼叫允許控制器之設計及系統效能分析,第二個主題是著重於智慧型網路壅塞控制器之製作。
      在第一主題中,我們著重於RBF網路效能之改善及以類神經網路為基礎的呼叫允許控制器之設計。RBF網路雖然廣泛地被應用於建構輸入及輸出的相關函數,但傳統的RBF網路卻遭遇了兩項困難:第一,RBF網路的初始值需要以錯誤嘗試法來決定;第二,當輸出期望值在某一區間有突然性的變化或者是固定值時,傳統的RBF網路效能會比較差。我們提出了一些方法來克服上述的缺點。首先使用新的隱藏層函數,並且採用ART的演算法來決定隱藏層神經元的個數,因此可以得到較佳的初始參數值及加權值,再經由梯度法則去修正這些加權值使得類神經網路的效能
    更好。接著我們把ART-RBF網路應用到ATM的呼叫允許控制器上。由於傳統的方法無法準確地估測出所需要的頻寬,往往造成頻寬過剩或細胞遺失率過高的現象。我們利用ART-RBF網路去估測所需要的頻寬,再來決定接受或拒絕新連線的要求。由於我們的方法所估測出的值較為準確,因此更能有效地控制ATM網路上的品質。
      在第二主題中,我們研究網路壅塞的方法並且提出了一個結合模糊類神經網路與速率回饋方式的壅塞控制系統。傳統的方法是直接觀測細胞停留在ATM佇列中長度的大小,假如長度大於預設值時,ATM會通知來源端以固定值來調降傳送的速率,然而這個方法很難根據網路壅塞程度而求出一個合適的來源端速率。為了改善此缺點,我們使用模糊類神經網路來控制來源端所送出的速率,經由訓練後,網路可以根據佇列中的長度及其隨時間的變化來預估出細胞遺失比率,進而推算出來源端下一次所應該傳送的速率。
      總之,本論文對類神經網路的架構及效能提出了一些改善的方法,並且將類神經網路應用在ATM網路的交通流量控制上。我們提出一些實用的控制機制,並且經由實驗模擬,顯示這些機制比傳統方法有效。
    摘要(英)  ATM (Asynchronous Transfer Mode) networks were deemed the best choice for multimedia communication. The traditional mode was replaced because ATM can provide varied traffic types and QoS (quality of service). Maintaining QoS, however, requires a flexible traffic control, including call admission control and congestion control. Traditional approaches fail to estimate the required bandwidth and cell loss rate precisely. To alleviate these problems, we employ AI methods to improve the capability of estimated bandwidth and predicted cell loss rate. This thesis aims to apply neural network techniques to ATM traffic control and consists of two parts. The first part concerns a neural-based call admission control, while the second part presents an intelligent congestion control for ATM networks.
     In the first part, we focus on the improvement of RBF (Radial basis function) networks and the design of a neural-based call admission control. RBF networks have been widely used for modeling a function from given input-output patterns. However, two difficulties are encountered with traditional RBF networks. One is that the initial configuration of a RBF network needs to be determined by a trial-and-error method. The other is that the performance suffers from some difficulties when the desired output has abrupt changes or constant values in certain intervals. We propose a novel approach to overcome these difficulties. New kernel functions are used for hidden nodes, and the number of nodes is determined automatically by an ART-like algorithm. Parameters and weights are initialized appropriately, and then tuned and adjusted by the gradient descent method to improve the performance of the network. Then, we employ ART-RBF networks to design and implement a call admission control. Traditional approaches fail to estimate appropriately the required bandwidth, leading to a waste of bandwidth or a high cell loss rate. To alleviate the problem, we employ ART-RBF networks to estimate the required bandwidth, and thus a new connection request can then be accepted or rejected. Because of the more accurate estimation on the required bandwidth, the proposed method can provide a better control on quality of service for ATM networks.
     In the second part, we propose a neural-fuzzy rate-based feedback congestion control for ATM networks. Traditional methods perform congestion control by monitoring the queue length. The source rate is decreased by a fixed rate when the queue length is greater than a predefined threshold. However, it is difficult to get a suitable rate according to the degree of traffic congestion. We employ a neural-fuzzy mechanism to control the source rate. Through learning, cell loss can be predicted from the current value and the derivative of the queue length. Then an explicit rate is calculated and the source rate is controlled appropriately.
     In summary, we have proposed improvements on architecture and performance of neural networks, and applied neural networks to traffic control for ATM networks. We have developed some control mechanisms which, through simulations, have been shown to be more effective than traditional methods.
    關鍵字(中)
  • 呼叫允許控制器
  • 模糊類神經網路
  • 類神經網路
  • 壅塞控制器
  • 關鍵字(英)
  • Neuro-Fuzzy Networks
  • Neural Networks
  • Congestion Control
  • Call Admission Control
  • 論文目次 Contents
    Chapter 1 Introduction…1
    1.1 Motivations…2
    1.2 Organization of this dissertation…3
    Chapter 2 ART-RBF Networks…7
    2.1 Introduction…8
    2.2 ART-RBF Networks…10
       2.2.1 Traditional RBF Networks…11
       2.2.2 SRBF Networks…12
    2.3 Architecture of Proposed Networks…13
    2.4 Initial Configuration…15
    2.5 Derivation of Initial Parameters…20
    2.6 Learning Rules…22
    2.7 Simulation Results…25
    2.8 Summary…41
    Chapter 3 Call Admission Control…44
    3.1 Introduction…44
    3.2 Model-Based Aggregate Characteristics…45
    3.3 Measurement-Based Control…47
    3.4 Ratio-Based Control…49
    3.5 Weight-Based Control…51
    3.6 Neural-Based Control…56
    3.7 Performance Comparison…58
       3.7.1 Static Case…58
       3.7.2 Dynamic Case…60
    3.8 Resource Management…67
    3.9 Simulation Results…70
    3.10 Summary…81
    Chapter 4 Congestion Control…82
    4.1 Introduction…83
    4.2 Traditional Contol Schemes…84
        4.2.1 Proportional Rate Control Algorithm…84
        4.2.2 Enhanced Proportional Rate Control Algorithm…87
    4.3 Overview of the Proposed Controller…88
    4.4 A Predictor Based On Neural-Fuzzy Networks…91
    4.5 Source Rate Calculation…97
    4.6 Simulation Results…100
    4.7 Summary…107
    Chapter 5 Leaky Bucket Regulator…108
    5.1 Introduction…108
    5.2 System Overview…110
    5.3 Cell Loss Calculation…111
    5.4 Learning Rules…114
    5.5 Rate Regulation…116
    5.6 Simulation Results…117
    5.7 Summary…122
    Chapter 6 Conclusions…123
    6.1 Review of Main Contributions…123
    6.2 Future Work…124
    Bibliography…126
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    口試委員
  • 何正信 - 召集委員
  • 林進燈 - 委員
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  • 詹寶珠 - 委員
  • 李錫智 - 指導教授
  • 口試日期 2003-01-15 繳交日期 2003-02-11

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