臺灣博碩士論文加值系統

由於網際網路的便利，帶動網路的蓬勃發展，網路的應用已成為企業主要成功因素之一。然而需求愈殷切，網路也愈龐大和複雜，為有效管理整體網路，就必須有合適的網路管理系統，才能有效執行網路管理(Network Management)的五項功能FCAPS。然而，使用者所關心的主要是網路的訊務(traffic)流量及延遲現象。
本研究之主要目的在建構一有效預測ADSL寬頻網路的訊務流量和頻寬使用率的預測模式，以期作為網路規劃建設及訊務管理之依據。研究之數據來自網管系統及相關資訊系統，使網管系統與類神經網路結合，做到網管不僅提供網路的效能管理，同時也具備訊務預測能力，以增強網管功效。為了克服網路使用訊務量來源的不確定因素，本文以類神經倒傳遞網路(Back-propagation Network)作為網路訊務流量之預測，並將影響輸出流量的輸入變數因素區分為費率類別、傳輸速率、客戶屬性、日期時間四方面。類神經網路具備強大學習能力、非線性、具容錯之優點，預期研究之結果，將可進一步知道使用者特性與訊務流量之關係，以提供決策者作為寬頻網路商場競爭之策略運用。

In recent years, the convenience of Internet impels the prosperous development of network. The application of network has become one of the key factors that lead the enterprise to success. However, the more the demand is, the more complicated and bulky the network becomes. In order to manage the integrated network efficiently, it is necessary to have a suitable network management system to execute the five FCAPS functions of network management. Nevertheless, what the users mainly concern about is the traffic flow and delay of the network.
The main objective of this study is to build a predictive model to effectively forecast the traffic flow and the bandwidth utilization of ADSL broadband network so that it could be the basis for planning, construction and traffic management of network. The data for research come from network management system and related information systems. Conjoining network management system with neural networks enables network management system not only to provide the efficient management of network, but also owns the ability to predict the traffic load so the power of network management system could be enhanced. To overcome the uncertainty of the traffic load source of network utilization, this study makes use of back-propagation network to predict the traffic flow of network. The input variables that influence the output flow are classified into four types including ISP-class, transmission rate, customer characteristics and date-time. Neural networks have powerful learning capabilities and the advantages of non-linearity and fault-tolerance. The results from the traffic flow forecasting could furthermore realize the relationship between the user characteristics and traffic flow so as to provide the decision-makers as strategy exertion for the competition of broadband network market.

書名頁…………………………………………………………………………i
中文提要………………………………………………………………………ii英文提要……………………………………………………………………iii
目錄……………………………………………………………………………iv
表目錄………………………………………………………………………vi
圖目錄………………………………………………………………………vii
一、 緒論 1
1.1 研究背景 1
1.2 研究動機 1
1.3 研究目的 2
1.4 論文架構 3
1.5 預期貢獻 4
二、 文獻探討 6
2.1 類神經網路應用於寬頻網路之相關文獻 6
2.2 寬頻網路管理系統之探討 7
2.2.1 寬頻、寬頻網路、網路管理之定義 7
2.2.2 網路管理之基本目標 8
2.2.3 網路管理應具備之基本功能FCAPS 9
2.2.4 網路管理協定之標準 11
2.3 寬頻網路之關鍵技術 16
2.3.1 寬頻接取網路(Access Network)現況 16
2.3.2 ADSL用戶成長狀況分析 18
2.3.3 ADSL使用者連線狀況分析 18
2.3.4 ADSL技術概述 19
2.3.5 傳輸網路(Transmission Network) 20
2.3.6 寬頻交換網路(Switch Network) 22
2.4 整合網路管理系統(INMS) 22
三、 類神經網路簡介 24
3.1 類神經網路發展史 24
3.2 類神經網路之基本理論 25
3.3 類神經網路之基本單元架構 26
3.4 類神經網路的分類 26
3.5 倒傳遞網路簡介 27
3.5.1 倒傳遞網路之基本架構 28
3.5.2 倒傳遞網路之演算法 28
3.6 類神經網路之優缺點 32
四、 寬頻網路訊務預測之研究方法與設計 34
4.1 系統分析與設計 34
4.2 研究之流程 36
4.3 本研究類神經網路架構 37
4.4 輸入/輸出變數資料分析 38
4.5 網路參數設定 41
4.5.1 DY22322D訊務流量預測 45
4.5.2 DY22322E訊務流量預測 49
4.5.3 APT0321D訊務流量預測 53
4.5.4 APT0321E訊務流量預測 57
4.6 研究結果分析 65
五、 結論與建議 67
5.1 結論 67
5.2 對業界之建議 67
5.3 未來研究之建議 67
5.4 研究之限制 68
參考文獻 69

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