類神經模糊決策

Abstract

基於非同步傳輸技術的寬頻整合服務網路,被期許未來能支援具多樣性 統計特 徵及服務品質的各種多媒體資訊傳輸。為了滿足各種服務品質的 需求,有效率的 排程方法在非同步傳輸網路的交通流量控制中佔有很重 要的一環。在一般的排程 方法中,單調速率法是較為簡單而容易用於高速 網路的方法,但它不像期限導向法 可以達到較高的系統使用率。然而,期 限導向法的計算卻很複雜,難以以硬體製作 出來。混合排程法結合了單調 速率法和期限導向法,所以能同時具有這兩種方法 的優點。在這篇論文 中,我們使用混合排程法以期在硬體的限制下達到最高的系 統使用率。 因為混合排程法的測試並沒有一個可供分析的快速判斷方法,所以我 們 提出一個類神經模糊決策樹,它可以在及時環境中迅速提供測試結果。類 神經 模糊決策樹把遺傳演算法及類神經模糊網路結合到決策樹中。模擬 結果顯示,在 非同步傳輸網路中,對於混合排程法的測試,類神經模糊決 策樹能提供一個快速有 效的解決方案。 Future broadband integrated services networks based on the Asynchronous Transfer Mode (ATM) technology are expected to support multiple types of multimedia information with diverse statistical characteristics and quality of service (QoS) requirements. To meet these requirements, efficient scheduling methods are important for traffic control in the ATM networks. Among the general scheduling schemes, the rate monotonic algorithm is simple to be used in high-speed networks, but does not attain high system utilization as the deadline driven algorithm does. However, the deadline driven scheme is computationally complex and hard to be implemented by the hardware. The mixed scheduling algorithm is the combination of the rate monotonic algorithm and the deadline driven algorithm; thus it can provide most of the benefits of these two algorithms. In this thesis, we use the mixed scheduling algorithm to achieve high system utilization under the hardware constraint. Because there is no analytic method for the schedulability test of the mixed scheduling, we propose a GA-based Neural Fuzzy Decision Tree (GANFDT) to realize it in a real-time environment. The GANFDT combines the genetic algorithm (GA) and a neural fuzzy network with a binary classification tree. This approach exploits the power of the classification tree. Simulation results show that the GANFDT provides an efficient way to carry out the mixed scheduling in the ATM networks.