在一些應用中，例如:在不確定的隨機訊號與系統模型過程裡， H∞濾波器提供了強
健的工具作為訊號評估、參數預測，及輸出回授設計用。在這篇論文裡，我們考慮使用
一應用例作為開發基於H∞濾波器的輸出回授控制系統之說明。
在使用現代化寬頻網路時，於資料暴量與長距離有關之MPEG傳輸流量的評估，藉
以改善通訊網路有關的服務品質。具趨勢和週期性的MPEG視頻訊號傳輸量可用此提出
之隨機狀態空間模組完整地取得，藉以改進預估的準確度。一般而言在即時性應用裡之
隨機過程是在不確定性或未備妥的狀況下，本論文提出一種遞迴的H∞ 濾波演算法作為
長距離傳輸量參數評估。提出不同於以前的估測方法，此處使用針對分別地預估I、P 和
B 訊號框來估測MPEG視頻傳輸量。利用真正的MPEG 通信量數據來模擬出結果，顯
示出時間變化的趨勢、週期性成分，和長距離相依性的屬性可被用來作為評估與擷取方
法。提出的方法比一些適應性的類神經網路方法具有更優越性能和更低的複雜性，例如
TDNN、NARX, 及Elman類神經網路。長距離傳輸量之MPEG視頻準確和迅速的估測，
有助於動態的寬頻分配與更好的網路使用率，以及使用更少的貯列等來改善高速封包網
路的傳輸量管理機制。
最後，我們將著眼於強健H∞輸出回饋控制在非線性隨機連續延時系統問題上，使
用Takagi和Sugeno模糊模式來描述的狀態相依的噪音之研究。基於這種模糊的方法，採
模糊控制單元和模糊狀態預估器以確保H∞強健穩定性，同時藉解雙線性矩陣不等式實
現於非線性隨機系統。

Abstract
In practical applications such as uncertainties in the system model and signal statistics,
the H∞ filter has been proven to be a robust tool for signal prediction, parameter
estimation, and output feedback control system design. In this dissertation, we consider
a novel application of the H∞ filter and the development of the H∞−filter-based output
feedback control system.
(i) The application is to predict the burst and long-range dependent MPEG traffic flow
in a modern wideband network so as to improve the related QoS of the communication
network. The trend and periodic characteristics of MPEG video traffic are fully captured
by a proposed stochastic state-space dynamic model, which includes traffic parameters
in the state vector, to improve prediction accuracy. As the statistics of the underlying
processes are either unavailable or uncertain in real-time applications, a recursive H∞
filtering algorithm is proposed to estimate traffic parameters for long-range prediction.
Unlike previous prediction schemes, which predict I, P and B frames separately, the
proposed scheme predicts the composite MPEG video traffic. Simulation results based
on real MPEG traffic data show that the time-varying trend, the periodic components,
and the long-range dependence property can be splendidly predicted and captured by the
proposed method. The proposed scheme has superior performance and lower complexity
than some other adaptive neural network methods, such as TDNN, NARX, and Elman
neural networks, in long-range prediction. With accurate and fast long-range prediction
of MPEG video traffic, it is useful for dynamic bandwidth allocation with better network
utilization and less queue occupancy to improve traffic management in high speed packet
networks.
(ii) Finally, we shall study the robust H∞ output feedback control problem for nonlinear
stochastic continuous-time time-delay systems with state-dependent noise represented
by Takagi and Sugeno fuzzy model. Based on the fuzzy approach, the fuzzy controller and
the fuzzy state estimator which guarantee H∞ robustness performance for the considered
nonlinear stochastic systems can be obtained by solving bilinear matrix inequalities.

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