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系统辨识: 新的模式、挑战及机遇

王乐一 赵文虓

王乐一, 赵文虓. 系统辨识: 新的模式、挑战及机遇. 自动化学报, 2013, 39(7): 933-942. doi: 10.3724/SP.J.1004.2013.00933
引用本文: 王乐一, 赵文虓. 系统辨识: 新的模式、挑战及机遇. 自动化学报, 2013, 39(7): 933-942. doi: 10.3724/SP.J.1004.2013.00933
WANG Le-Yi, ZHAO Wen-Xiao. System Identification: New Paradigms, Challenges, and Opportunities. ACTA AUTOMATICA SINICA, 2013, 39(7): 933-942. doi: 10.3724/SP.J.1004.2013.00933
Citation: WANG Le-Yi, ZHAO Wen-Xiao. System Identification: New Paradigms, Challenges, and Opportunities. ACTA AUTOMATICA SINICA, 2013, 39(7): 933-942. doi: 10.3724/SP.J.1004.2013.00933

系统辨识: 新的模式、挑战及机遇

doi: 10.3724/SP.J.1004.2013.00933
基金项目: 

国家自然科学基金(61134013, 61104052, 61273193)资助

详细信息
    通讯作者:

    王乐一

System Identification: New Paradigms, Challenges, and Opportunities

Funds: 

Supported by National Natural Science Foundation of China (61134013, 61104052, 61273193)

  • 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.
  • [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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  • 收稿日期:  2012-07-04
  • 修回日期:  2012-11-07
  • 刊出日期:  2013-07-20

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