System Identification: New Paradigms, Challenges, and Opportunities
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摘要: 钱学森教授曾对系统给出一个简明的定义: 系统是指依一定秩序相互联系的一组事物. 一般说来, 系统辨识可以认为是利用已知先验信息和输入-输出数据来建立系统数学模型的科学. 经过半个多世纪的发展, 系统辨识已成为一个定义较为明确、发展相当成熟的研究领域, 在思想方法、理论基础、实际应用等诸多方面都有丰富的研究成果. 进入新世纪, 伴随着科学技术的突飞猛进, 新学科、新研究领域不断涌现, 给传统的系统辨识带来了新的挑战与机遇. 因而, 从这个角度说, 系统辨识仍是一个年轻的、朝气蓬勃的学科. 本文将讨论系统辨识在新机遇下一些具有潜力的重要方向, 提出一些值得关注的热点问题, 以此为楔入点, 抛砖引玉, 希望能引发进一步的讨论.Abstract: The traditional paradigm of system identification employs prior information on system structures and environments and input/output observation data to derive system models. Extensive research and development on its methodologies, theoretical foundation, algorithms, verifications, and applications over the past half century have established a mature field with a rich literature and substantial benchmark applications. However, rapid advancement in science, technology, engineering, and social medias has ushered in a new era of systems science and control in which challenges and opportunities are abundant for system identification. In this sense, system identification remains an exciting, young, viable, and critical field that mandates new paradigms to meet such challenges. This article points out some potentially important aspects of system identification in these new paradigms, suggests some worthy areas of research focus, and most importantly opens the forum for further discussions.
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[1] Zadeh L A. On the identification problem. IEEE Transactions on Circuits and Systems, 1956, 3(4): 277-281 [2] Cai Ji-Bin. System Identification. Beijing: Beijing Institute of Technology Press, 1989 (蔡季冰. 系统辨识. 北京: 北京理工大学出版社, 1989) [3] Guo Lei, Cheng Dai-Zhan, Feng De-Xing. Introduction to Control Theory: from Basic Concepts to Research Frontiers, Beijing: Science Press, 2005 (郭雷,程代展, 冯德兴. 控制理论导论从基本概念到研究前沿. 北京: 科学出版社, 2005) [4] [4] Ljung L, Vicino A. Guest editorial: special issue on system identification. IEEE Transactions on Automatic Control, 2005, 50(10): 1473 [5] [5] Gevers M. A personal view of the development of system identification: a 30-year journey through an exciting field. IEEE Control Systems Magazine, 2006, 26(6): 93-105 [6] [6] Ljung L. Perspectives on system identification. In: Proceedings of the 17th IFAC World Congress. Seoul, South Korea: IFAC, 2008. 7172-7184 [7] [7] Ljung L, Hjalmarsson H, Ohlsson H. Four encounters with system identification. European Journal of Control, 2011, 17(5-6): 449-471 [8] [8] Ljung L. System Identification: Theory for the User. Englewood Cliffs, NJ: Prentice-Hall, 1987 [9] [9] Chen H F, Guo L. Identification and Stochastic Adaptive Control. Boston, MA: Birkhuser, 1991 [10] Ljung L. Analysis of recursive stochastic algorithms. IEEE Transactions on Automatic Control, 1977, 22(4): 551-575 [11] Chen H F. Recursive identification for Wiener model with discontinuous piece-wise linear function. IEEE Transactions on Automatic Control, 2006, 51(3): 390-400 [12] Kushner H J, Yin G. Stochastic Approximation and Recursive Algorithms and Applications (2nd edition). New York: Springer-Verlag, 2003 [13] Chen H F, Zhu Y M. Stochastic approximation procedures with randomly varying truncations. Science in China, Series A, 1986, 29(9): 914-926 [14] Chen H F. Stochastic Approximation and Its Applications. Dordrecht, The Netherlands: Kluwer Academic, 2002 [15] Akaike H. A new look at the statistical model identification. IEEE Transactions on Automatic Control, 1974, 19(6): 716 -723 [16] Burnham K P, Anderson D R. Multimodel inference: understanding AIC and BIC in model selection. Sociological Methods and Research, 2004, 33(2): 261-304 [17] Rissanen J. Modeling by shortest data description. Automatica, 1978, 14(5): 465-471 [18] Zheng W X, Feng C B. Identification of stochastic time lag systems in the presence of colored noise. Automatica, 1990, 26(4): 769-779 [19] Zheng W X, Feng C B. A bias-correction method for indirect identification of closed-loop systems. Automatica, 1995, 31(7): 1019-1024 [20] Soderstrom T. Accuracy analysis of the Frisch scheme for identifying errors-in-variables systems. IEEE Transactions on Automatic Control, 2007, 52(6): 985-997 [21] Sderstrm T, Soverini U, Mahata K. Perspectives on errors-in-variables estimation for dynamic systems. Signal Processing, 2002, 82(8): 1139-1154 [22] Forssel U, Ljung L. Closed-loop identification revisited. Automatica, 1999, 35(7): 1215-1241 [23] Obinata G, Brian D O A. Model Reduction for Control System Design. London: Springer, 2001 [24] Kalman R E. Design of a self-optimising control system. Transactions on ASME, 1958, 80: 468-478 [25] Astrm K J, Wittenmark B. On self tuning regulators. Automatica, 1973, 9(2): 185-199 [26] Guo L. Convergence and logarithm laws of self-tuning regulators. Automatica, 1995, 31(3): 435-450 [27] Durrett R. Probability: Theory and Examples (Cambridge Series in Statistical and Probabilistic Mathematics) (4th edition). Cambridge: Cambridge University Press, 2010 [28] Zhou Tong. Introduction to Control-Oriented System Identification. Beijing: Tsinghua University Press, 2004 (周彤. 面向控制的系统辨识导论. 北京: 清华大学出版社, 2004 ) [29] Milanese M, Belforte G. Estimation theory and uncertainty intervals evaluation in presence of unknown but bounded errors: linear families of models and estimators. IEEE Transactions on Automatic Control, 1982, 27(2): 408-414 [30] Milanese M, Vicino A. Optimal estimation theory for dynamic systems with set membership uncertainty: an overview. Automatica, 1991, 27(6): 997-1009 [31] Mkil P M, Partington J R, Gustafsson T K. Worst-case control-relevant identification. Automatica, 1995, 31(12): 1799-1819 [32] Chen J, Gu G X. Control-Oriented System Identification: An H Approach. New York: Wiley-Interscience, 2000 [33] Smith R S, Dahleh M A [Editor]. The modeling of uncertainty in control systems. In: Proceedings of the 1992 Santa Barbara Workshop. Berlin, Germany: Springer-Verlag, 1994 [34] Helmicki A J, Jacobson C A, Nett C N. Control oriented system identification: a worst-case/deterministic approach in H. IEEE Transactions on Automatic Control, 1991, 36(10): 1163-1176 [35] Gu G X, Khargonekar P P. Linear and nonlinear algorithms for identification in H. IEEE Transactions on Automatic Control, 1992, 37(7): 953-963 [36] Gu G X, Khargonekar P P. A class of algorithms for identification in H. Automatica, 1992, 28(2): 299-312 [37] Chen J, Nett C N. The Caratheodory-Fejer problem and H/l_1 identification: a time domain approach. IEEE Transactions on Automatic Control, 1995, 40(4): 729-735 [38] Chen J, Nett C N, Fan M K H. Worst case system identification in H: validation of a priori information, essentially optimal algorithms, and error bounds. IEEE Transactions on Automatic Control, 1995, 40(7): 1260-1265 [39] Zhou T, Kimura H. Time domain identification for robust control. Systems and Control Letters, 1993, 20(3): 167-178 [40] Chen J. Frequency-domain tests for validation of linear fractional uncertain models. IEEE Transactions on Automatic Control, 1997, 42(6): 748-760 [41] Poolla K, Khargonekar P, Tikku A, Krause J, Nagpal K. A time-domain approach to model validation. IEEE Transactions on Automatic Control, 1994, 39(5): 951-959 [42] Smith R S, Doyle J C. Model validation: a connection between robust control and identification. IEEE Transactions on Automatic Control, 1992, 37(7): 942-952 [43] Anderson T W. The Statistical Analysis of Time Series. New York: Wiley, 1971 [44] Serfling R J. Approximation Theorems of Mathematical Statistics. New York: Wiley, 1980 [45] Bohlin T. On the maximum likelihood method of identification. IBM Journal of Research and Development, 1970, 14(1): 41-51 [46] Wang L Y. Uncertainty, information and complexity in identification and control. International Journal of Robust and Nonlinear Control, 2000, 10(11-12): 857-874 [47] Casini M, Garulli A, Vicino A. Time complexity and input design in worst-case identification using binary sensors. In: Proceedings of the 46th IEEE Conference on Decision and Control. New Orleans, LA, USA: IEEE, 2007. 5528-5533 [48] Milanese M, Vicino A. Information-based complexity and nonparametric worst-case system identification. Journal of Complexity, 1993, 9(4): 427-446 [49] Wang L Y, Yin G G. Towards a harmonic blending of deterministic and stochastic frameworks in information processing. Robustness in Identification and Control, Lecture Notes in Control and Information Sciences Volume 245. London: Springer-Verlag, 1999. 102-116 [50] Wang L Y, Yin G G. Persistent identification of systems with unmodeled dynamics and exogenous disturbances. IEEE Transactions on Automatic Control, 2000, 45(7): 1246-1256 [51] Dahleh M A, Theodosopoulos T V, Tsitsiklis J N. The sample complexity of worst-case identification of FIR linear systems. Systems and Control Letters, 1993, 20: 157-166 [52] Chong C Y, Kumar S P. Sensor networks: evolution, opportunities, and challenges. Proceedings of the IEEE, 2003, 91(8): 1247-1256 [53] Wang J D, Zheng W X, Chen T W. Identification of linear dynamic systems operating in a networked environment. Automatica, 2009, 45(12): 2763-2772 [54] Zhang Q, Zhang J F. Distributed parameter estimation over unreliable networks with Markovian switching topologies. IEEE Transactions on Automatic Control, 2012, 57(10): 2545-2560 [55] Liu X H, Goldsmith A. Wireless communication tradeoffs in distributed control. In: Proceedings of the 42nd IEEE Conference on Decision and Control. Maui, HI: IEEE, 2003. 688-694 [56] Yuksel S, Basar T. Optimal signaling policies for decentralized multicontroller stabilizability over communication channels. IEEE Transactions on Automatic Control, 2007, 52(10): 1969-1974 [57] Wang L Y, Zhang J F, Yin G. System identification using binary sensors. IEEE Transactions on Automatic Control, 2003, 48(11): 1892-1907 [58] Wang L Y, Yin G, Zhang J F, Zhao Y L. System Identification with Quantized Observations. Boston, MA: Birkhuser, 2010 [59] Wang L Y, Li C Y, Yin G, Guo L, Xu C Z. State observability and observers of linear-time-invariant systems under irregular sampling and sensor limitations. IEEE Transactions on Automatic Control, 2011, 56(11): 2639-2654 [60] He T, Lu X L, Wu X Q, Lu J A, Zheng W X. Optimization-based structure identification of dynamical networks. Physica A: Statistical Mechanics and Its Applications, 2013, 392(4): 1038-1049 [61] Zhou T, Wang Y L. Causal relationship inference for a large-scale cellular network. Bioinformatics, 2010, 26(16): 2020- 2028 [62] Wang Y L, Zhou T. A relative variation-based method to unraveling gene regulatory networks. PLoS ONE, 2012, 7(2): e31194 [63] Bolouri H, Bower J M. Computational Modeling of Genetic and Biochemical Networks. Cambridge, Mass: MIT Press, 2001 [64] Billings S A. Identification of nonlinear systems a survey. IEE Proceedings D: Control Theory and Applications, 1980, 127(6): 272-285 [65] Sjberg J, Zhang Q H, Ljung L, Benveniste A, Delyon B, Glorennec P Y, Hjalmarsson H, Juditskys A. Nonlinear black-box modeling in system identification: a unified overview. Automatica, 1995, 31(12): 1691-1724 [66] Verhaegen M, Westwick D. Identifying MIMO Wiener systems using subspace model identification methods. Signal Processing, 1996, 52(2): 235-258 [67] Bai E W, Reyland J Jr. Towards identification of Wiener systems with the least amount of a priori information on the nonlinearity. Automatica, 2008, 44(4): 910-919 [68] Bai E W. An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear system. Automatica, 1998, 34(3): 333-338 [69] Ninness B, Gibson S. Quantifying the accuracy of Hammerstein model estimation. Automatica, 2002, 38(12): 2037- 2051 [70] Zhu Y. Estimation of an N-L-N Hammerstein-Wiener model. Automatica, 2002, 38: 1607-1614 [71] Bai E W, Li K. Convergence of the iterative algorithm for a general Hammerstein system identification. Automatica, 2010, 46(11): 1891-1896 [72] Zhao W X, Chen H F. Adaptive tracking and recursive identification for Hammerstein systems. Automatica, 2009, 45: 2773-2783 [73] Zhao W X, Chen H F. Identification of Wiener, Hammerstein, and NARX systems as Markov chains with improved estimates for their nonlinearities. Systems and Control Letters, 2012, 61(12): 1175-1186 [74] Mu B Q, Chen H F. Recursive identification of Wiener-Hammerstein systems. SIAM Journal on Control and Optimization, 2012, 50(5): 2621-2658 [75] Roll J, Nazin A, Ljung L. Nonlinear system identification via direct weight optimization. Automatica, 2005, 41(3): 475- 490 [76] Zhao Y L, Wang L Y, Yin G G, Zhang J F. Identification of Wiener systems with binary-valued output observations. Automatica, 2007, 43(10): 1752-1765 [77] Wang L Y, Yin G G. Asymptotically efficient parameter estimation using quantized output observations. Automatica, 2007, 43(7): 1178-1191 [78] Katayama T. Subspace Methods for System Identification. London: Springer, 2005 [79] Van Overschee P, de Moor B. A unifying theorem for three subspace system identification algorithms. Automatica, 1995, 31(12): 1853-1864 [80] Vidal R. Recursive identification of switched ARX systems. Automatica, 2008, 44(9): 2274-2287 [81] Wang J, Chen T. Parameter estimation of periodically switched linear systems. IET Control Theory and Applications, 2012, 6(6): 768-775 [82] Zames G, Lin L, Wang L Y. Fast identification n-widths and uncertainty principles for LTI and slowly varying systems. IEEE Transactions on Automatic Control, 1994, 39(9): 1827-1838 [83] Tan P N, Steinbach M, Kumar V. Introduction to Data Mining. Boston, MA: Addison-Wesley, 2006 [84] Sayeed A M. A signal modeling framework for integrated design of sensor networks. In: Proceedings of the 2003 IEEE Workshop Statistical Signal Processing. St. Louis, MO, USA: IEEE, 2003. 7 [85] Vapnik V N. Statistical Learning Theory. New York: Wiley-Interscience, 1998 [86] Vapnik V N. The Nature of Statistical Learning Theory (Information Science and Statistics for Engineering and Information Science) (2nd edition). New York: Springer Verlag, 1999 [87] Vidyasagar M. Learning and Generalization: With Applications to Neural Networks (2nd edition). London: Springer, 2003 [88] Kolmogorov A N. On some asymptotic characteristics of completely bounded spaces. Doklady Akademii Nauk SSSR, 1956, 108: 385-389 (in Russian) [89] Pinkus A. N-Widths in Approximation Theory. New York: Springer-Verlag, 1985 [90] Traub J F, Wasilkowski G W, Wozniakowski H. Information-based Complexity. New York: Academic Press, 1988 [91] Wang L Y, Yin G G, Zhang J F, Zhao Y L. Space and time complexities and sensor threshold selection in quantized identification. Automatica, 2008, 44(12): 3014-3024 [92] Lin L, Wang L Y, Zames G. Time complexity and model complexity of fast identification of continuous-time LTI systems. IEEE Transactions on Automatic Control, 1999, 44(10): 1814-1828 [93] Poolla K, Tikku A. On the time complexity of worst-case system identification. IEEE Transactions on Automatic Control, 1994, 39(5): 944-950 [94] Tse D C N, Dahleh M A, Tsitsiklis J N. Optimal asymptotic identification under bounded disturbances. IEEE Transactions on Automatic Control, 1993, 38(8): 1176-1190 [95] Zames G. On the metric complexity of causal linear systems: \varepsilon-entropy and \varepsilon-dimension for continuous time. IEEE Transactions on Automatic Control, 1979, 24(2): 222-230 [96] Cramer H. Mathematical Methods of Statistics. Princeton NJ: Princeton University Press, 1946 [97] Frieden B R. Science from Fisher Information: A Unification. Cambridge: Cambridge University Press, 2004 [98] Hannan E J, Deistler M. The Statistical Theory of Linear Systems. New York: John Wiley and Sons, 1988 [99] Cover T M, Thomas J A. Elements of Information Theory. New York: Wiley-Interscience, 1991 [100] Gallager R G. Information Theory and Reliable Communication. New York: John Wiley and Sons, 1968 [101] Gersho A, Gray R M. Vector Quantization and Signal Compression. Norwell, MA: Kluwer Academic Publishers, 1991 [102] Sayood K. Introduction to Data Compression (2nd edition). San Francisco: Morgan Kaufmann, 2000 [103] Davis M D, Sigal R, Weyuker E J. Computability, Complexity, and Languages (2nd edition). San Diego: Academic Press, 1994 [104] Zurek W H. Complexity, Entropy, and the Physics of Information. Reading, MA: Addison-Wesley, 1990 [105] Gevers M. Towards a joint design of identification and control? Essays on Control: Perspectives in the Theory and Its Applications. Boston, MA: Birkhuser, 1993. 111-151 [106] Ljung L. System Identification Toolbox for Use with Matlab, User's Guide (5th edition). Natick, MA: The MathWorks, Inc. Sherborn, Mass, 2000 1-350 [107] Garnier H, Gilson M, Laurain V. The CONTSID toolbox for Matlab: Extensions and latest developments. In: Proceedings of the 15th IFAC Symposium on System Identification. Saint-Malo, France, 2009. 735-740 [108] Ljung L. Educational aspects of identification software user interfaces. In: Proceedings of the 13th IFAC Symposium on System Identification. Rotterdam, The Netherlands: IFAC, 2003. 1590-1594 [109] Rivera D E, Lee H, Braun M W, Mittelmann H D. Plant-friendly system identification: a challenge for the process industries. In: Proceedings of the 13th IFAC Symposium on System Identification. Rotterdam, Netherlands: IFAC, 2003. 917-922
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