以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：107

、訪客IP：3.144.233.89

姓名	王志湖(Chih-Hu Wang)  查詢紙本館藏	畢業系所	電機工程學系
論文名稱	隨機系統之H ∞ 濾波器及H ∞ 控制器設計 (H∞ Filter and H∞ Control Design for Stochastic System)
相關論文	★ 運算放大器之自動化設計流程及行為模型研究 ★ 匯流排上的時間延遲及交談失真的偵錯設計技巧 ★ 適用於自動測試機台的時間產生器 ★ 混波測試匯流排的量測學 ★ 高速連結之時序與資料回復 ★ 基於IEEE 1057之類比數位轉換器量測技術 ★ 應用於高畫質電視之載波回復電路架構 ★ 單晶片測試機之前端驅動電路設計 ★ 系統晶片類比數位轉換器測試之數位信號處理程式庫 ★ A 2.5V,0.35um,2.5Gbps 傳送接收器設計 ★ 內建式類比數位/數位類比轉換器線性度之自我測試 ★ 高準確度及低成本之電壓量測技術 ★ 應用於ATSC VSB時脈回復之全數位延遲線迴路 ★ 適用於晶片間通訊之高速傳輸介面 ★ 內建式類比數位轉換器之自我校正方法 ★ 高速序列傳輸之量測技術
檔案	[Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載] 本電子論文使用權限為同意立即開放。 已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。 請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。
摘要(中)	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。
摘要(英)	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.
關鍵字(中)	★ 濾波器 ★ 控制 ★ 隨機系統 ★ 模糊	關鍵字(英)	★ Fuzzy ★ Filter ★ Stochastic System ★ Control
論文目次	Contents 1 Introduction 1 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.1 Applications of Linear Robust State Estimation . . . . . . . . 1 1.1.2 Nonlinear Robust H1 Observed-based Output Feedback Control 6 1.2 Organization of this Dissertation . . . . . . . . . . . . . . . . . . . . 7 2 Application of the Linear H1 Filter to Long-Range Prediction for Real-Time MPEG Video Tra¢ c 9 2.1 A brief review of MPEG frame types . . . . . . . . . . . . . . . . . . 10 2.2 State-space signal model for MPEG-encoded video sequence and the prediction problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.3 Long-range tra¢ c prediction based on H1 lter . . . . . . . . . . . . 19 2.4 Simulation examples . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.4.1 Prediction by using dynamic neural networks . . . . . . . . . . 24 2.4.2 Comparisons of prediction performance . . . . . . . . . . . . . 26 2.4.3 Statistical analysis of H1 prediction . . . . . . . . . . . . . . 31 2.4.4 Dynamic Bandwidth Allocation Using H1 Prediction . . . . . 37 3 Nonlinear H1 Output Feedback Control of Stochastic Time-Delay T-S Fuzzy Model with State-Dependent Noise 42 3.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.2 H1 Control System Design . . . . . . . . . . . . . . . . . . . . . . . 49 4 Conclusions and Discussions 56
參考文獻	[1] S. C. Liew and D. C. Y. Tse “A control-theoretic approach to adapting VBR compressed video for transport over a CBR communications channel,” IEEE Trans. Networking, vol. 6, no. 1, pp. 42-55, 1998. [2] P. Pancha and M. E. Zarki, “MPEG coding for variable bit rate video trans- mission,”IEEE Commun. Mag., vol. 32, no. 5, pp. 54-66, May 1994. [3] E. Elwalid, D. Heyman, T. Lakshman, A. Weiss, and D Mitra, “Fundamental bounds and approximations for ATM multiplexers with application to video teleconferencing,” IEEE J. Select. Areas Commun., vol. 13, no. 6, pp. 1004- 1016, Aug. 1995. [4] J. Beran, R. Sherman, M. S. Taqqu, andW.Willinger, “Long-range dependence in variable-bit-rate video tra¢ c,” IEEE Trans. Commun., vol. 43, no. 2, pp. 1566-1579, Feb.-March-April, 1995. [5] D. P. Heyman and T. V. Lakshman, “Source models for VBR broadcast-video tra¢ c,”IEEE/ACM Trans. Networking, vol. 4, no. 1, pp. 40-48, Feb. 1996. [6] D. P. Heyman and T. V. Lakshman, “What are the implications of long-range dependence for VBR-video tra¢ c engineering,”IEEE/ACM Trans. Networking, vol. 4, no. 3, pp. 301-317, Jun. 1996. [7] H. Heeke, “Tra¢ c control algorithm for ATM networks,”IEEE Trans. Circuits System Video Technol., vol. 3, no. 3, pp. 182-189, June 1993. [8] M. Conti, E. Gregori, and A. Larsson, “Study of the impact of MPEG-1 corre- lations on video-sources statistical multiplexing,”IEEE J. Select. Areas Com- mun., vol. 14, no. 7, pp. 1455-1471, Sep. 1996. [9] M. Grossglauser and J.C. Bolot, “On the relevance of long-range dependence in network tra¢ c,”IEEE/ACM Trans. Networking, vol. 7, no. 5, pp. 629-640, Oct. 1999. [10] S. Chong, S. Li and J. Ghosh, “Predictive dynamic bandwidth allocation for e¢ cient transport of real-time VBR video over ATM,”IEEE J. Select. Areas Commun., vol. 13, no. 1, pp. 12-23, Jan. 1995. [11] P. Chang and J. Hu, “Optimal nonlinear adaptive prediction and modelling of MPEG video in ATM networks using piplined recurrent neural networks,” IEEE J. Select. Areas Commun., vol. 15, no. 6, pp. 1087-1100, Aug. 1997. [12] A. Adas, “Using adaptive linear prediction to support real-time VBR video under RCBR network service model,”IEEE Trans. Networking, vol. 6, no. 5, pp. 635-644, Oct. 1998. [13] S. J. Yoo, “E¢ cient tra¢ c prediction scheme for real-time VBR MPEG video transmission over high-speed networks,”IEEE Trans. Broadcasting, vol. 48, no. 11, pp. 10-18, March 2002. [14] A. D. Doulamis, N. D. Doulamis, and S. D. Kollias, “An adaptable neural- network model for recursive nonlinear tra¢ c prediction and modeling of MPEG video sources,”IEEE Trans. on Neural Networks, vol. 14, no. 1, pp. 150-166, Jan. 2003. [15] A. Bhattacharya, A. G. Parlos, and A. F. Atiya, “Prediction of MPEG- coded video source tra¢ c using recurrent neural networks,”IEEE Trans. Signal Processing, vol. 51, no. 8, pp. 2177-2190, August 2003. [16] A. Abdennour, “Evaluation of neural network architectures for MPEG-4 video tra¢ c prediction," IEEE Trans. Broadcasting, vol. 52, no. 2, pp. 184-192, June 2006. [17] N. Ansari, H. Liu, Y. Q. Shi, and H. Zhao, “On Modeling MPEG Video Traf- cs,”IEEE Trans. Broadcasting, vol. 48, no. 4, pp. 337-347, Dec. 2002. [18] K. M. Nagpal and P. P. Khargonekar, “Filtering and smoothing in an H1 setting,” IEEE Trans. Automatic Control, vol. 36, no. 2, pp. 152-160, Feb. 1991. [19] X. Shen and L. Deng, “Discrete H1 lter design with application to speech enhance,”ICASSP-95, vol. 2, pp. 1504-1507 1995. [20] B. Hassibi, A. H. Sayed, and T. Kailath, “Linear estimation in Krein spaces— part I: Theory,”IEEE Trans. Automat. Contr., vol. 41, no. 1, pp. 18-33, Jan. 1996. [21] B. Hassibi, A. H. Sayed, and T. Kailath, “Linear estimation in Krein spaces— Part II: Application,”IEEE Trans. Automat. Contr., vol. 41, no. 1, pp. 34-49, Jan. 1996. [22] P. J. Brockwell and R. A. Davis, Introduction to Time Series and Forecasting, Springer, New York, 1996. [23] Z. Fan and P. Mars, “Access ‡ow control scheme for ATM network using neural network-based tra¢ c prediction,”IEE Proc. Commu., vol. 144, no 5, pp.295- 300, Oct. 1997. [24] M. R. Pickering and J. F. Arnold, “A perceptually e¢ cient VBR rate control algorithm,” IEEE Trans. Image Processing, vol. 3, no. 5, pp. 527-532, Sept. 1994. [25] A. A. Tarraf, I. W. Habib, and T. N. Saadawi, “A novel neural network tra¢ c enforcement mechanism for ATM network,”IEEE J. Select. Areas Commun., vol. 12, no. 6, pp. 1088-1095, Aug. 1994. [26] N. M. Mara h, Y. Q. Zhang, and R. L. Pickholtz, “ Modeling and queuing analysis of variable-bit-rate coded video sources in ATM network,”IEEE Trans. Circuit Sept. Video Technol., vol. 4, no. 2, pp. 121-128, Apr. 1994. [27] V. Catanaia, G. Ficili, S. Palazzo, and D. Panno, “A comparative analysis of fuzzy versus conventional policing mechanisms for ATMnetworks,”IEEE/ACM Trans. Networking, vol. 4, no. 3, pp. 449-459, Jun. 1996. [28] P. Chemuil, J. Kbalfet, and M. Lebourgges, “A fuzzy control approach for adaptive tra¢ c routing,”IEEE Commun. Mag., vol. 33, no. 7, pp. 70-76, Jul. 1995. [29] On-line homepage: http://www-info3.informatik.uni-wuerzburg.de/MPEG/. [30] F. H. P. Fitzek and M. Reisslein, “MPEG-4 and H.263 video traces for network performance evaluation,” IEEE Network, vol.15, no. 6, Nov.- Dec. 2001, pp. 40-54. Traces available at homepage: http://www-tkn.ee.tu- berlin.de/research/trace/trace.html and fttp://www.eas.asu.edu/trace. [31] H. Demuth, M. Beale, and M. Hagan, Neural Network Toolbox 5: User’s Guide, The MathWorks, 2007. [32] H. Li, G. Liu, Z. Zhang, and Y. Li, “Adaptive scene-detection algorithm for VBR video stream,”IEEE Trans. Multimedia, vol. 6, no. 4, pp. 624-633, August 2004. [33] S. G. Wang, H. Y. Yeh, and P. N. Roschke, “Robust control for structural systems with parametric and unstructured uncertainties,” Proceedings of the American Control Conference, pp. 1109-1114, June 25-27, 2001. [34] T. Shen and K. Tamura, “Robust H1 control of uncertain nonlinear system via state feedback,”IEEE Transactions on Automatic Control, vol. 40, no. 4, pp. 766-769, April 1995. [35] B. S. Chen, C. S. Tseng, and H. J. Uang, “Mixed H2=H1 fuzzy output feedback control design for nonlinear dynamic systems: an LMI approach,”IEEE Trans. Fuzzy Systems, vol. 8, no. 3, pp. 249-265, June 2000. [36] M. Safonov and M. Athans, “Robustness and computational aspects of non- linear stochastic estimators and regulators,”IEEE Transactions on Automatic Control, vol. 23, no. 4, pp.717-725, Aug. 1978. [37] F. Yang, Z. Wang, and D.W.C. Ho, “Robust mixed H2=H1 control for a class of nonlinear stochastic systems,”IEE Proceedings, Control Theory and Appli- cations, vol. 153, no. 2, pp. 175-184, March 2006. [38] T. J. Tarn and Y. Rasis, “Observers for nonlinear stochastic systems,”IEEE Trans. Automatic Control, vol. 21, no. 4, pp.441-448, Aug. 1976. [39] C. D. Charalambous and S. M. Diouadi, “Stochastic nonlinear minimax ltering in continuous-time,”Proceedings of the 40th IEEE Conference on Decision and Control, vol. 3, pp. 2520-2525, Dec. 2001. [40] W. Zhang and B. S. Chen, “Robust H1 ltering for nonlinear stochastic sys- tems,”IEEE Trans. Signal Processing, vol. 53, no. 2, pp. 589-598, Feb. 2005. [41] F. Carravetta and G. Mavelli, “Asymptotic properties of an output-feedback suboptimal control scheme for stochastic bilinear systems,”Proceeding of the 2004 American Control Conference, Boston, Massachusetts, pp. 3146-3151, June 30-July 2, 2004. [42] W. H. Chen, Y. T. Chang, and B. S. Chen, “Nonlinear stochastic H2=H1 output feedback control under state-dependent noise,”IEEE Conference on Decision and Control, pp. 302-307, Dec. 2006. [43] T. Takagi and M. Sugeno, “Fuzzy identi cation of systems and its applications to modeling and control,”IEEE Trans. Syst., Man, Cybern., vol. SMC-15, no. 1, pp. 116-132, Jan. 1985. [44] C. S. Tseng, “Robust Fuzzy Filter Design for a Class of Nonlinear Stochastic Systems,”IEEE Trans. Fuzzy Systems, vol. 15, no. 2, pp. 261-274, April 2007. [45] L. Hu, W. Zhao, and S. Shao, “Robust stochastic stabilization and robust H1 control for uncertain stochastic fuzzy systems,”The 14th IEEE International Conference on Fuzzy Systems, pp. 254-259, May 2005. [46] S. C. Chen, Robust and Optimal Estimation for Uncertain Stochastic Fuzzy T-S Models, Master thesis, Department of Electrical Engineering, Chung Hua University, August 2007. [47] S. Boyd, L. E. Ghaoui, E. Feron, and V. Balakrishnan, Linear Matrix Inequal- ities in System and Control Theory, Philadelphia, PA, SIAM, 1994. [48] R. Z. Has’minski¼¬, Stochastic Stability of Di¤erential Equations, Netherlands, International Publishers B. V. ,1980.
指導教授	蘇朝琴、劉建男 (C. C. Su、Chien-Nan Jimmy Liu)	審核日期	2009-7-28
推文	facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu
網路書籤	Google bookmarks   del.icio.us   hemidemi   myshare