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：23

、訪客IP：18.224.59.107

姓名	林似霖(Shih-Lin Lin)  查詢紙本館藏	畢業系所	機械工程學系
論文名稱	數據分析中盲源分離之研究 (Study on Blind Source Separation during Data Analysis)
相關論文	★ 自動平衡裝置在吊扇上之運用 ★ 以USB通訊界面實現X-Y Table之位置控制 ★ 液體平衡環在立式轉動機械上之運用 ★ 液流阻尼裝置設計與特性之研究 ★ 液晶電視喇叭結構共振異音研究 ★ 液態自動平衡環之研究 ★ 抑制牙叉式機械臂移載時產生振幅之設計 ★ 立體拼圖式組合音箱共振雜音消除之設計 ★ 電梯纜繩振動抑制設計研究 ★ 以機器學習導入電梯生產結果預測之研究 ★ 新環保冷媒R454取代R410A冷媒迴轉式單缸壓縮機效能分析與可靠性驗證 ★ 高速銑削Al7475-T7351的銑削參數與基因演算法研究 ★ 自動化鞋型切削機之設計與實現 ★ 以FPGA為基礎之精密位置控制IC ★ CNC三維圓弧插補器 ★ PID與模糊控制在營建工程自動化的探討
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)	數據分析是不可或缺，因為它是研究過程的一個重要階段，我們可以分析數據有新的發現。在這論文中有兩種好的數據分析方法，一種是獨立成份分析(ICA)，另一種是經驗模態分解(EMD)，本論文分成幾個部分，第一部分是應用獨立成份分析改善胎兒心電圖，經由獨立成份分析方法改善的結果可以更了解胎兒心跳的情況，第二部分是應用獨立成份分析於通訊保密上，由於獨立成份分析在估測的過程中可能會有失真與誤差的產生，所以估測出的結果會有相位相反和振幅大小不相等的現象，使得通訊保密還原的訊號失真，本研究中提出改良式的獨立成份分析，改良的獨立成份分析可以估測出真正的相位和振幅，使得保密的訊號可還原與原本一樣的訊號，第三部分是解決雜訊干擾問題，當我們要分析的數據受雜訊嚴重干擾，而且我們又無法用很多感知器去量測的數據分析問題，在此提出獨立成份分析和經驗模態分解結合的方法，當數據是高雜訊的情況時，利用兩個感知器可以分離出多個原始訊號，此方法是先用獨立成份分析進行處理，把雜訊和原始混合的訊號分開，再將原始混合的訊號進行經驗模態分解的分析，結果顯示此方法確實改善雜訊干擾問題。
摘要(英)	Data are the only link we have with unexplained reality; therefore, data analysis is the only way through which we can find out the underlying processes of any given phenomenon. One of the most important goals of scientific research is to understand nature. Data analysis is a critical link in the scientific research cycle of observation, analysis, synthesizing, and theorizing. There are two commonly used data analysis methods. One is independent component analysis (ICA), and the other is empirical mode composition (EMD). However, it is difficult to conduct blind source separation (BSS) during data analysis. The first issue that needs to be dealt with is than the signals separated by traditional ICA shows opposite phase and unequal amplitude, leading to aliasing after the original signals are retrieved. The second major problem occurs when the number of sensors is greater than or equal to the number of sources; blind source separation becomes a difficult part of the underdetermined problem. These two types of problems have routinely been considered as an obstacle for source separation. The aim of this study is to solve two BBS problems. An algorithm method is proposed which can improve these problems. The modified ICA algorithm has applications in many different fields, such as for fetal electroencephalograms (EEGs) secure communications in chaotic systems, and chaos control in communications. Here the combined ICA-EMD is applied to low SNR simulated data, low SNR length-of-day data analysis and secure communications in chaotic systems. It is demonstrated that the combination of ICA and EMD can achieve better results. The two methods complement each other. The results show that these methods are an effective data analysis tool and have great potential for application in many different fields.
關鍵字(中)	★ 盲源分離 ★ 獨立成份分析 ★ 經驗模態分解 ★ 數據分析	關鍵字(英)	★ Blind Source Separation ★ ICA ★ EMD ★ Data analysis
論文目次	COVER ABSTRACT (CHINESE)........................................I ABSTRACT (ENGLISH).......................................II ACKNOWLEDGMENT..........................................III TABLE OF CONTENTS.........................................i LIST OF FIGURES.........................................iii LIST OF TABLES............................................v ABBREVIATIONS & SYMBOLS..................................vi CHAPTER 1 INTRODUCTION 1.1 Blind Source Separation Problem.......................1 1.2 Independent Component Analysis........................2 1.3 Empirical Mode Decomposition..........................3 1-4 Main Contribution of this Thesis and the organizational Structure...............................4 1-5 Overview of this Thesis...............................7 CHAPTER 2 APPLICATION OF ICA IN THE FETUS ECG 2.1 Introduction..........................................9 2.2 FastICA Theory.......................................11 2.3 Results and Discussion...............................13 2.4 Conclusion...........................................17 CHAPTER 3 MODIFIED ICA 3.1 A Modified Method for Blind Source Separation........18 3.2 Application of Modified ICA to Secure Communications in Chaotic Systems.......................................32 3.3 A New Method for Chaos Control in Communication Systems...............................................49 3.4 Detection of the Real Primary Original Signal from a Noisy Source.............................................67 CHAPTER 4 COMBINATION OF ICA AND EMD 4.1 Data Analysis using a Combination of ICA and EMD.....77 4.2 Application of ICA-EEMD to Secure Communications in Chaotic Systems.......................................94 CHAPTER 5 CONCLUSION AND FUTURE WORK 5.1 Conclusions.............................................104 5.2 Future work.........................................106 REFERENCES..............................................109 PUBLICATION LISTS.......................................124
參考文獻	[1] J.-F. Cardoso, “Blind signal separation: statistical srinciples.” Proceedings of the IEEE, Vol. 86, No. 10, pp. 2009-2025, 1998. [2] C.-J. Lu, T.-S. Lee, C.-C. Chiu “Financial time series forecasting using independent component analysis and support vector regression,” Decision Support Systems, Vol. 47, pp. 115-125, 2009. [3] J. Karhunen. “Neural approaches to independent component analysis and source separation,” In Proc. 4th European Symp. Artificial Neural Networks, ESANN’96, Bruges, Belgium, pp.249-266, Apr. 1996. [4] H. Sahlin, H. Broman, “Separation of real-world signals,” Signal Processing, Vol.64, No.2, pp.103-113, 1998. [5] G. J. Erickson, J. T. Rychert and C. R. Smith. “Difficults applying recent blind source separation techniques to EEG and MEG,” Proceedings of the 17th International Workshop on Maxiumum Entropy and Bayesian Methods of Statistical Analysis, Boise, Idaho, pp.209-222, 1997. [6] J. Karhumen, A. Hyvärinen, “Application of neural blind separation to signal and image processing,” In Proc ICASSP. Germany, Munich, pp.131-134, 1997. [7] S. Makeig, T. P. Jung, A. J. Bell, “Blind separation of auditory event-related brain reponses into independent components,” In Proc Natl Acad Sci, Vol.94, pp.10979-10984, 1997. [8] A. Benveniste, “Goursat M. Blind Equalizers,” IEEE Trans on Commun, Vol.32, No.8, pp.871-883, 1984. [9] S. Makeig, M. Westerfield, T-P. Jung, S. Enghoff, J. Townsend, E. Courchesne & T. J. Sejnowski “Dynamic brain sources of visual evoked responses,” Science Vol.295, pp.690-694, 2002. [10] J. Herault & C. Jutten “Space or time adaptive signal processing by neural network models,” Neural Networks for Computing, AIP Conference Proceedings Vol.151, pp.207-211, 1986. [11] P. Comon, “Independent component analysis a new concept?” Signal Process., Vol.36, pp.287-314, 1994. [12] A. J. Bell and T. J. Sejnowski, “An information maximisation approach to blind separation and blind deconvolution,” Neural Comput., Vol.7, No.6, pp.1129-1159, 1995. [13] J.-F. Cardoso, B. H. Laheld, “Equivariant adaptive source separation,” IEEE Transactions on Signal Processing, Vol.44, No.12 pp.3017-3030, Dec 1996 [14] D. T. Pham and P. Garat, “Blind separation of mixture of independent sources through aquasi-maximum likelihood approach,” IEEE Transactions on Signal Processing, Vol.45, No.7, pp.1712-1725, Jul 1997. [15] T. W. Lee, Independent Component Analysis: Theory and Applications, Kluwer Academic Publishers, Boston, 1998. [16] A. Hyvärinen, “Sparse code shrinkage: Denoising of non-Gaussian data by maximum likelihood estimation,” Neurocomputing Vol.11, No.7, pp. 1739-1768, 1999. [17] A. Hyvärinen and E. Oja, “Independent Component Analysis: Algorithms and Applications,” Neural Netw. Vol.13, No.4-5, pp.411-430, 2000. [18] A. Hyvärinen, “Fast and robust fixed-point algorithms for independent component analysis,” IEEE Trans. Neural Netw. Vol.10, No.3, pp. 626-634, 1999b. [19] A. Hyvärinen and E. Oja, “A fast fixed-point algorithm for independent component analysis,” Neural Comput. Vol.9, No.7, pp. 1483-1492, 1997. [20] A. Hyvärinen, J. Karhunen and E. Oja, Independent Component Analysis Wiley, New York, 2001. [21] N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, E. H. Shih, Q. Zheng, C. C. Tung, & H. H. Liu, “The empirical mode decomposition method and the Hilbert spectrum for non-stationary time series analysis,” Proc. Roy. Soc. Lond. A Vol. 454, pp. 903-995, 1998. [22] Z. Wu, N. E Huang, Ensemble Empirical Mode Decomposition: a noise-assisted data analysis method. Advances in Adaptive Data Analysis. 1(1) 1-41 (2009) [23] N. E. Huang, Z. Wu, “A review on Hilbert-Huang transform: method and its applications to geophysical studies,” Reviews of Geophysics, Vol.46, No. RG2006, 2008. [24] Z. Wu, N. E Huang, S. R. Long, C.-K. Peng, “On the trend, detrending, and the variability of nonlinear and non-stationary time series,” Proc. Natl. Acad. Sci. USA. Vol. 104, pp. 14889-14894, 2007. [25] N. E. Huang, , M. L. Wu, S. R. Long, S. S. Shen, W. D. Qu, P. Gloersen, & K. L. Fan, “A confidence limit for the Empirical Mode Decomposition and Hilbert Spectral Analysis,” Proc. Roy. Soc. Lond. A Vol. 459, pp.2317-2345, 2003. [26] W. Huang, Z. Shen, N. E. Huang, & Y. C. Fung, “Engineering analysis of biological variables: An example of blood pressure over 1 day,” Proc. Natl. Acad. Sci. USA. Vol. 95, pp. 4816-4821, 1998. [27] D. A.T. Cummings, R. A. Irizarry, N. E. Huang, T. P. Endy, A. Nisalak, K. Ungchusak & D. S. Burke, “Travelling waves in the occurrence of dengue haemorrhagic fever in Thailand,” Nature Vol. 427, pp. 344-347, 2004. [28] Z. Wu, N. E. Huang, S. R. Long, & C.-K. Peng, “On the trend, detrending, and the variability of nonlinear and non-stationary time series,” Proc. Natl. Acad. Sci. USA. Vol. 104, pp. 14889-14894, 2007. [29] C. J. James and C. W. Hesse, “Independent component analysis for biomedical signals,” Physiol. Meas. Vol. 26, No.R15–R39, 2005. [30] E. D. Lauro, S. D. Martino and M. Falanga A. Ciaramella and R. Tagliaferri, “Complexity of time series associated to dynamical systems inferred from independent component analysis,” Physical Review E Vol.72, No.046712, 2005. [31] F. Rojas, C. G. Puntonet, M. R.-Álvarez, I. Rojas, and R. M.-Clemente, “Blind source separation in post-nonlinear mixtures using competitive learning, simulated annealing, and a genetic algorithm,” IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews, Vol. 34, NO. 4, pp.407-416, NOV. 2004, [32] J. M. Górriza, C. G. Puntonetb, F. Rojasb, R. Martinc, S. Hornilloc, E. W. “Lang Optimizing blind source separation with guided genetic algorithms,” Neurocomputing, Vol.69 pp.1442-1457, 2006. [33] L. C. Khor, W. L. Woo and S. S. Dlay, “Nonlinear blind signal separation with intelligent controlled learning,” IEE Proc.-Vis. Image Signal Process., Vol. 152, No. 3, pp.297-306, June 2005, [34] S. Fiori, “Fixed-point neural independent component analysis algorithms on the orthogonal group,” Future Generation Computer Systems, Vol.22, pp.430-440, 2006. [35] J. J. Gonzalez de la Rosa, C.G. Puntonet, I. Lloret, “An application of the independent component analysis to monitor acoustic emission signals generated by termite activity in wood,” Measurement, Vol.37, pp.63-76, 2005. [36] W. Li, F. Gu, A. D. Ball, A. Y. T. Leung and C. E. Phipps “A study of the noise from diesel engines using the independent component analysis,” Mechanical Systems and Signal Processing, Vol. 15, No.6, pp. 1165-1184, 2001. [37] Y. Li, S.-I. Amari, A. Cichocki, D. W. C. Ho, & S. Xie, “Underdetermined blind source separation based on sparse representation,” IEEE Transactions on Signal Processing, Vol. 54, pp. 423-437, 2006. [38] A. Cichocki, & S. Amari,, Adaptive blind signal and image processing: learning algorithms and applications, Wiley, New York, 2002 [39] J. Herault, and C. Jutten, “Space or time adaptive signal processing by neural network models,” Neural Networks for Computing, AIP Conference Proceedings, Vol.151, 1986, pp. 207-211. [40] C. Jutten, and J. Herault, “Blind separation of sources, past I: An adaptive algorithm based on neuromimetic architecture,” Signal Processing, Vol. 22, pp. 1-10, 1991. [41] J. Karhunen, and J. Joutsensalo, “Representaion and separation of signals using nonlinear PCA type learning,” Neural Networks, Vol.7, pp. 223-227, 1994, [42] A. Cichocki, R. Unbehauen, and E. Rummert, “Robust learning algorithm for blind separation of signals,” Electronics Letters, Vol.30, pp. 1386-1387, 1994. [43] P. Comon, “Contrasts for multichannel blind deconvolution,” Signal Processing Letters, Vol.3,No.7, pp. 209-211, 1996. [44] R. Linsker, “Local synaptic learning rules suffice to maximize mutual information in a linear network,” Neural Computation, Vol. 4, pp. 691-702, 1992. [45] B. Widrow, J. R. Glover, J. M. McCool, J. Kauntiz, C. S. Williams, R. H. Hearn, J. R. Zeidler, JR E. Dong, and R. C. Goodlin, “Adaptive noise cancelling: principles and applications,” IEEE Transactions on Biomedical Engineering, Vol.BME-63, pp. 1692-1716, 1975. [46] V. Zarzoso and A. K. Nandi, “Noninvasive fetal electrocardiogram extraction: blind source separation versus adaptive noise cancellation,” IEEE Transactions on Biomedical Engineering, Vol.48, pp. 12-18, 2001. [47] P. P. Kanjilal,S. Palit and G. Saha, “Fetal ECG extraction from single-channel maternal ECG using singular value decomposition,” IEEE Transactions on Biomedical Engineering, Vol. 44, pp. 51-59, 1997. [48] L. De Lathauwer, B. De Moor and J. Vandewalle, “Fetal electrocardiogram extraction by blind source separation,” IEEE Transactions on Biomedical Engineering, Vol.47, pp. 567-572, 2000. [49] M. Shao, K. E. Barner and M. H. Goodman, “An interference cancellation algorithm for noninvasive extraction of transabdominal fetal electroencephalogram (TaFEEG),” IEEE Transactions on Biomedical Engineering, Vol.51, pp. 471-483, 2004. [50] S. L. Horner and W. M. Holls, “Real-time signal processing of an intrauterine catheter’s fetal electrocardiogram,” Digital Signal Processing, Vol.13, pp.368-395, 2003. [51] A. Khamene and S. Negahdaripour, “A new method for the extraction of fetal ECG from the composite abdominal signal,” IEEE Transactions on Biomedical Engineering, Vol. 47, pp.507 -516, 2000. [52] K. Assaleh, and H. Al-Nashash, “A novel technique for the extraction of fetal ECG using polynomial networks,” IEEE Transactions on Biomedical Engineering, Vol. 52, pp.1148-1152, 2005. [53] M. G. Jafari, and J. A. Chambers, “Fetal electrocardiogram extraction by sequential source separation in the wavelet domain,” IEEE Transactions on Biomedical Engineering, Vol.52, pp.390-400, 2005, [54] M. Kotas, “Projective filtering of time-aligned ECG beats,” IEEE Transactions on Biomedical Engineering, Vol.51, pp.1129-1139, 2004. [55] F. S. Najafabadi, E. Zahedi, M. A. Mohd Ali, “Fetal heart rate monitoring based on independent component analysis” Computers in Biology and Medicine Vol.36, pp. 241–252, 2006. [56] S. Comania, D. Mantinic, P. Pennesic, A. Lagattab, G Cancellieri, “Independent component analysis: fetal signal reconstruction from magnetocardiographic recordings” Computer Methods and Programs in Biomedicine Vol.75, 1pp. 63—177, 2004. [57] 1. L. D. Lathauwer, B. D. Moor, J. Vandewalle, “Fetal Electrocardiogram Extraction by Blind Source Subspace Separation” IEEE Trans. on Biomedical Engineering, Vol. 47, No. 5, pp.567-572, 2000. [58] 2. J. Maddox, “Cocktail party effect made tolerable,” Nature, Vol. 369, pp. 517, 1994. [59] 3. L. Molgedey, & H. G. Schuster, “Separation of a mixture of independent signals using time delayed correlations,” Phys. Rev. Lett. Vol. 72, pp. 3634-3637, 1994. [60] 4. E. Seifritz, F. Esposito, F. Hennel, H. Mustovic, J. G. Neuhoff, et al., “Spatiotemporal pattern of neural processing in the human auditory cortex,” Science, Vol. 297, pp. 1706-1708, 2002. [61] M. Alrubaiee, M. Xu, S. K. Gayen, M. Brito, & R. R. Alfano, “Three-dimensional optical tomographic imaging of scattering objects in tissue-simulating turbid media using independent component analysis,” Appl. Phys. Lett. Vol. 87, No. 191112, 2005. [62] M. Alrubaiee, M. Xu, S. K. Gayen, & R. R.Alfano, “Localization and cross section reconstruction of fluorescent targets in ex vivo breast tissue using independent component analysis,” Appl. Phys. Lett. Vol. 89, No.133902, 2006. [63] J. B. Tenenbaum, V.de Silva, & J. C. Langfoed, “A globl geometric framework for nonlinear dimensionality reduction,” Science Vol. 290, pp. 2319-2323, 2000. [64] E. Mjolsness, & D. DeCoste, “Machine learning for science: state of the art and future prospects,” Science Vol. 293, pp. 2051-2055, 2001. [65] M. S. Lewicki, “Efficient coding of natural sounds,” Nature Neuroscience Vol. 5, pp. 356-362, 2002. [66] F. C. Meinecke, A. Ziehe, J. Kurths, & K.-R. , “Measuring phase synchronization of superimposed signals,” Phys. Rev. Lett. Vol. 94, No. 084102, 2005. [67] Lauro, E. De, Martino, S. De & Falanga, M. Complexity of time series associated to dynamical systems inferred from independent component analysis. Phys. Rev. E 72, No. 046712 (2005). [68] X. Huang, S. Y. Lee, E. Prebys, & R. Tomlin, “Application of independent component analysis to fermilab booster,” Physical Review Special Topics-Accelerators & Beams, Vol. 8, No.. 064001, 2005. [69] M. Laubach, J. Wessberg, & M. A. L. Nicolelis, “Cortical ensemble activity increasingly predicts behaviour outcomes during learning of a motor task,” Nature, Vol. 405, pp. 567-571, 2000. [70] H. Stögbauer, A. Kraskov, S. A. Astakhov, & P. Grassberger, “Least dependent component analysis based on mutual information,” Phys. Rev. E Vol. 70, No. 066123, 2004. [71] N. M. Abramson, Information Theory and Coding. McGraw-Hill, New York, 1963. [72] A. Feinstein, Foundations of Information Theory. McGraw-Hill, New York, 1958. [73] M.J.D.Powell, “Restart Proceducres for the Conjugate Gradient Method”, Mathematical Programming Vo.12, pp.242-254, 1977. [74] B. Widrow and S. D. Stearns, Adaptive Signal Processing. Englewood Cliffs, NJ: Prentice-Hall, 1985. [75] E. N. Lorenz, “Deterministic nonperiodic flow,” J. Atmos. Sci. Vol. 20 pp. 130-141, 1963. [76] T. L. Carroll, “Communicating with use of filtered synchronized chaotic signals,” IEEE Trans. Circuits Syst. Vol. 42, pp. 105-110, 1995. [77] N. F. Rulkov, M. M. Sushchik, L. S. Tsimring, and H. D. I. Abarbanel, “Generalized synchronization of chaos in directionally coupled chaotic Systems,” Phys. Rev. E, Vol. 51, pp. 980-994, 1995. [78] M. Lakshmanan, K. Murali, Chaos in nonlinear oscillators: controlling and cynchronization. World Scientific, Singapore 1996 [79] Lj. Kocarev, U. Parlitz, “Generalized synchronization, predictability, and equivalence of unidirectionally coupled dynamical systems,” Phys. Rev. Lett. Vol. 76, pp. 1816-1819, 1996. [80] K. Murali, M. Lakshmanan, “Secure communication using a compound signal from generalized synchronizable chaotic systems,” Phys. Lett. A Vol. 241, pp. 303-310, 1998. [81] K. Murali, “Digital signal transmission with cascaded heterogeneous chaotic system,” Phys. Rev. E, Vol. 63, No. 016217, 2000. [82] U. Parlitz, L.O. Chua, Lj. Kocarev, K.S. Halle, A. Shang, “Transmission of digital signals by chaotic synchronization,” Int. J. Bifurcation Chaos Vol. 2 No. 4, pp. 973-977, 1992. [83] K. M. Cuomo, A. V. Oppenheim, “Circuit implementation of synchronized chaos with applications to communications,” Phys. Rev. Lett. Vol. 71, pp. 65-68,1993. [84] H. Dedieu, M. P. Kennedy, M. Hasler, “Chaos shift keying: modulation and demodulation of a chaotic carrier using self-synchronizing Chua's circuits,” IEEE Trans. Circuits Syst. Vol. 40, pp. 634-642, 1993. [85] U. Parlitz, S. Ergezinger, “Robust communication based on chaotic spreading sequences,” Phys. Lett. A Vol. 188, No. 2, pp.146-150, 1997. [86] Lj. Kocarev, , K. Halle, , K. Eckert, , L. O. Chua, , U. Parlitz, “Experimental demonstration of secure communications via chaotic synchronization,” Int. J. Bifurcation Chaos Appl. Sci. Eng. Vol. 2, No.2, pp. 709-713, 1992. [89] K. Murali, M. Lakshmanan, “Transmission of signals by synchronization in a chaotic van der Pol–Duffing oscillator,” Phys. Rev. E Vol. 48 No. R1624-R1626, 1993. [90] C. W. Wu, L. O. Chua, “A simple way to synchronize chaotic systems with applications to secure communication systems,” Int. J. Bifurcation Chaos Appl. Sci. Eng. Vol. 3, pp. 1619-1627, 1993. [91] M. J. Ogorzalek, “Taming chaos. I. synchronization,” IEEE Trans. Circuits Syst. Vol. 40, pp. 639-699, 1993. [92] K. S. Halle, C. W. Wul, M. Itoh, L. O. Chua, “Spread spectrum communication through modulation of chaos,” Int. J. Bifurcation Chaos Appl. Sci. Eng. Vol. 3, pp. 469-477, 1993. [93] A. S. Dmitriev, A. I. Panas, S. O. Starkov, “Experiments on speech and music signals transmission using chaos,” Int. J. Bifurcation Chaos Appl. Sci. Eng. Vol. 5, No. 4, pp. 1249-1254, 1995. [94] A. V. Oppenheim, G. W.Wornell, S. H. Isabelle, K. M. Cuomo, Signal Processing in the Context of Chaotic Signals. IEEE Trans. Pp. 117-120, 1992. [95] L. J. Kocarev, et al., “Synchronization in Chaotic Systems,” Int. J. of Bifur Chaos Vol. 2, pp.709-713, 1992. [96] T. Shinbrot, C. Grebogi, E. Ott, J. A. Yorke, “Using small perturbation to control chaos,” Nature Vol. 363, pp. 411-417, 1993. [97] Y. Wang, Z. H. Guan, H. O. Wang, “Feedback and adaptive control for the synchronization of Chen system via a single variable,” Phys Lett A Vol. 312, pp. 34-40, 2003. [98] T. Zhou, L. Chen, R. Wang, “Excitation functions of coupling,” Phys Rev E Vol. 71, No. 066211, 2005. [99] E. M. Elabbasy, H. N. Agiza, M. M. El-Dessoky, “Adaptive synchronization of Lü system with uncertain parameters,” Chaos, Solitons & Fractals Vol. 21, pp. 657-67, 2004. [100] X. Tan, J. Zhang, Y. Yang, “Synchronizing chaotic systems using backstepping design,” Chaos, Solitons & Fractals Vol. 16, pp. 37-45, 2003. [101] T. Zhou, S. Zhang, “Echo waves and coexisting phenomena in coupled brusselators,” Chaos, Solitons & Fractals Vol. 13, pp. 621-32, 2002. [102] J. H. Par, O. M. Kwon. “A novel criterion for delayed feedback control of time-delay chaotic systems,” Chaos, Solitons & Fractals Vol. 23, pp. 495-501, 2005. [103] M. C. Ho, Y. C. Hung, “Synchronization of two different systems by using generalized active control,” Phys Lett A Vol. 301, pp. 424-428, 2002. [104] E. W. Bai, and K. E. Lonngren, “Sequential synchronization of two Lorenz systems using active control,” Chaos, Solitons and Fractals Vol. 11, pp. 1041-1044, 2000. [105] E. W. Bai, and K. E. Lonngren, “Synchronization of two Lorenz system using active control,” Chaos, Solitons and Fractals Vol. 8, pp. 51-58, 1997. [106] M. Chen, Z. Han, “Controlling and synchronizing chaotic Genesio system via nonlinear feedback control,” Chaos, Solitons & Fractals Vol. 17, pp. 709-16, 2003. [107] L. Huang, R. Feng, M. Wang, “Synchronization of chaotic systems via nonlinear control,” Phys Lett A Vol. 320, pp. 271-275, 2004. [108] J. Wang, T. Zhang, Y. Che, “Chaos control and synchronization of two neurons exposed to ELF external electric field,” Chaos, Solitons & Fractals Vol. 34, pp. 839-850, 2007. [109] B. Liu, L. Wang, Y. H. Jin, D. X. Huang, F. Tang, “Control and synchronization of chaotic systems by differential evolution algorithm,” Chaos, Solitons & Fractals Vol. 34, pp. 412-419, 2007. [110] J. Wang, T. Zhou, “Chaos synchronization based on contraction principle,” Chaos, Solitons & Fractals Vol. 33, pp.163-170, 2007. [111] J. H. Park, “Chaos synchronization between two different chaotic dynamical systems,” Chaos, Solitons & Fractals Vol. 27, pp. 549-554, 2006. [112] J. J. Yan, Y. S. Yang, T. Y. Chiang, C. Y. Chen, “Robust synchronization of unified chaotic systems via sliding mode control,” Chaos, Solitons & Fractals Vol. 31, pp. 947-954, 2007. [113] T. I. Chien, T. L. Liao, “Design of secure digital communication systems using chaotic modulation, cryptography and chaotic synchronization,” Chaos, Solitons & Fractals; Vol. 24, pp. 241-255, 2005. [114] H. T. Yau, “Nonlinear rule-based controller for chaos synchronization of two gyros with linear-plus-cubic damping,” Chaos, Solitons & Fractals Vol. 34, pp. 1357-1365, 2007. [115] G. M. Mahmoud, S. A. Aly, A. A. Farghaly, “On chaos synchronization of a complex two coupled,” Chaos, Solitons & Fractals Vol. 33, pp.178-87, 2007. [116] A. Ucar, K. E. Lonngren, E. W. Bai, “Synchronization of the unified chaotic systems via active control,” Chaos, Solitons & Fractals Vol. 27, pp. 1292-1297, 2006. [117] J. F. Cardoso, A, Soulumiac “Blind beamforming for non-Gausian signals,” IEE Proc. F. 1993;140:771-774. [118] Karl J. Àström, Bјörn Wittenmark, Adaptive Control. Addison Wesley (1995) [119] U. Parlitz and W. Lauterborn, “Superstructure in the Bifurcation Set of the Duffing Equation ”, Phys. Lett., vol. 107A, pp. 351-355, 1985. [120] I. M. Jánosi, R. Müller, “Empirical mode decomposition and correlation properties of long daily ozone records,” Phys. Rev. E Vol. 71, No. 056126, 2005. [121] A. M. Kakurin, I. I. Orlovsky. “Hilbert–Huang Transform in MHD Plasma Diagnostics,” Plasma Physics Reports Vol. 31, No.12, pp. 1054-1063, 2005. [122] A. Goska, A. Krawiecki, “Analysis of phase synchronization of coupled chaotic oscillators with empirical mode decomposition,” Phys. Rev. E Vol. 74, No. 046217, 2006. [123] Z. Peradzyński, J. Kurzyna, S. Mazouffre, et. al “Spectral analysis of Hall-effect thruster plasma oscillations based on the empirical mode decomposition,” Physics of Plasmas Vol. 12, No. 123506, 2005. [124] R. Balocchi, D. Menicucci, E. Santarcangelo, L. Sebastiani, A. Gemignani, B. Ghelarducci, & M. Varanini, “Deriving the respiratory sinus arrhythmia from the heartbeat time series using empirical mode decomposition,” Chaos, Solitons & Fractals Vol. 20, pp.171-177, 2004. [125] W.-S. Lam, , W. Ray, , P. N. Guzdar, & R. Roy, “Measurement of Hurst exponents for semiconductor laser phase dynamics,” Phys. Rev. Lett. Vol. 94, No. 010602, 2005. [126] R. Kozakov, C. Wilke & B. Bruhn, “Observation of intermittent states and nonlinear wave–wave interaction in neon glow discharges,” Phys. Lett. A Vol. 360, pp. 448-453, 2007. [127] J. B. Camp., J. K. Cannizzo, & K. Numata, “Application of the Hilbert-Huang transform to the search for gravitational waves,” Phys. Rev. D Vol. 75, No. 061101(R), 2007. [128] M. J. McKeown, R. Saab, R. Abu-Gharbieh. "A combined independent component analysis (ICA)/ empirical mode decomposition (EMD) method to infer corticomuscular coupling". IEEE International Conference on Neural Engineering, Arlington-USA, pp. 679-682, 2005. [129] M. J. McKeown, S. Palmer, W. Au, R. McCaig, R. Saab, R. Abu-Gharbieh. "Cortical muscle coupling in parkinson's disease (PD) bradykinesia". Journal of Neural Transmission, Suppl. 70, pp. 31-40, 2006. [130] R. S. Gross, “Combinations of Earth orientation measurements: SPACE2000, COMB2000, and POLE2000,” JPL Publication 01-2. Jet Propulsion Laboratory, Pasadena, CA. 2001. [131] Y.-C. Hung, C.-K. Hu, “Chaotic communication via temporal transfer entropy,” Phys. Rev. Lett. Vol. 101, No. 244102, 2008. [132] G. Álvarez, F. Montoya, M. Romera, G. Pastor, “Cryptanalysis of a chaotic secure communication system,” Phys. Rev. A Vol. 306, No.200-205, 2003. [133] G. Álvarez, F. Montoya, M. Romera, G. Pastor, “Cryptanalysis of a discrete chaotic cryptosystem using external key,” Phys. Rev. A Vol. 319, pp. 334-339, 2003.
指導教授	董必正(Pi-Cheng Tung)	審核日期	2009-7-26
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