System Identification Using Binary Sensors

IEEE Transactions on Automatic Control

Volumn 48, Issue 11, 2003, Pages 1892-1907

  Wang, Le Yi ^a   Zhang, Ji Feng ^b   Yin, G George ^c  

^a Wayne State University   (United States)

^b INSTITUTE OF SYSTEMS SCIENCE   (China)

^c WAYNE STATE UNIVERSITY   (United States)

Author keywords

Binary sensors; Estimation; System identification; Time complexity

Indexed keywords

ASYNCHRONOUS TRANSFER MODE; BANDWIDTH; ERROR ANALYSIS; OPTIMAL CONTROL SYSTEMS; REAL TIME SYSTEMS; SENSORS; TRAFFIC CONTROL;

BINARY VALUED SENSORS;

IDENTIFICATION (CONTROL SYSTEMS);

EID: 0345690126     PISSN: 00189286     EISSN: None     Source Type: Journal    
DOI: 10.1109/TAC.2003.819073     Document Type: Article

References (44)

1. H. Abut, Ed., "Vector Quantization," in IEEE Press Selected Repint Series. Piscataway, NJ: IEEE Press, 1990.
2. P. Billingsley, Convergence of Probability Measures. New York: Wiley, 1968.
3. E. R. Caianiello and A. de Luca, "Decision equation for binary systems: Application to neural behavior," Kybernetik, vol. 3, pp. 33-40, 1966.
4. H. F. Chen and L. Quo, Identification and Stochastic Adaptive Control. Boston, MA: Birkhäuser, 1991.
5. H. F. Chen and G. Yin, "Asymptotic properties of sign algorithms for adaptive filtering," IEEE Trans. Automat. Contr., vol. 48, pp. 1545-1556, Sept. 2003.
6. M. A. Dahleh, T. Theodosopoulos, and J. N. Tsitsiklis, "The sample complexity of worst-case identification of FIR linear systems," Syst. Control Lett., vol. 20, 1993.
7. C. R. Elvitch, W. A. Sethares, G. J. Rey, and C. R. Johnson Jr, "Quiver diagrams and signed adaptive filers," IEEE Trans. Acoust. Speech Signal Processing, vol. 30, pp. 227-236, Feb. 1989.
8. E. Eweda, "Convergence analysis of an adaptive filter equipped with the sign-sign algorithm," IEEE Trans. Automat. Contr., vol. 40, pp. 1807-1811, Oct. 1995.
9. W. Feller, An Introduction to Probability Theory and Its Applications, 3rd ed. New York: Wiley, 1968, vol. I.
10. _, An Introduction to Probability Theory and Its Applications, 2nd ed. New York: Wiley, 1971, vol. II.
11. A. Gersho, "Adaptive filtering with binary reinforcement," IEEE Trans. Inform. Theory, vol. IT-30, pp. 191-199, Mar. 1984.
12. A. Gersho and R. M. Gray, Vector Quantization and Signal Compression. Norwell, MA: Kluwer, 1992.
13. K. Gopalsamy and I. K. C. Leung, "Convergence under dynamical thresholds with delays," IEEE Trans. Neural Networks, vol. 8, pp. 341-348, Apr. 1997.
14. R. G. Hakvoort and P. M. J. Van den Hof, "Consistent parameter bounding identification for linearly parameterized model sets," Automatica, vol. 31, pp. 957-969, 1995.
15. A. L. Hodgkin and A. F. Huxley, "A quantitative description of membrane current and its application to conduction and excitation in nerve," J. Physiol., vol. 117, pp. 500-544, 1952.
16. P. R. Kumar, "Convergence of adaptive control schemes using least-squares parameter estimates," IEEE Trans. Automat. Contr., vol. 35, pp. 416-424, Apr. 1990.
17. A. N. Kolmogorov, "On some asymptotic characteristics of completely bounded spaces," Dokl. Akad. Nauk SSSR, vol. 108, pp. 385-389, 1956.
18. H. J. Kushner and G. Yin, Stochastic Approximation Algorithms and Applications. New York: Springer-Verlag, 1997.
19. L. Ljung, System Identification: Theory for the User. Upper Saddle River, NJ: Prentice-Hall, 1987.
20. M. Milanese and G. Belforte, "Estimation theory and uncertainty intervals evaluation in the presence of unknown but bounded errors: Linear families of models and estimators," IEEE Trans. Automat. Contr., vol. AC-27, pp. 408-414, Apr. 1982.
21. M. Milanese and A. Vicino, "Information-based complexity and non-parametric worst-case system identification," J. Complexity, vol. 9, pp. 427-446, 1993.
22. _, "Optimal estimation theory for dynamic systems with set membership uncertainty: An overview," Automatica, vol. 27, pp. 997-1009, 1991.
23. K. Pakdaman and C. P. Malta, "A note on convergence under dynamical thresholds with delays," IEEE Trans. Neural Networks, vol. 9, pp. 231-233, Feb. 1998.
24. D. Pollard, Convergence of Stochastic Processes. New York: Springer-Verlag, 1984.
25. K. Poolla and A. Tikku, "On the time complexity of worst-case system identification," IEEE Trans. Automat. Contr., vol. 39, pp. 944-950, May 1994.
26. K. Sayood, Introduction to Data Compression, 2nd ed. San Mateo, CA: Morgan Kaufmann, 2000.
27. R. J. Serfling, Approximation Theorems of Mathematical Statistics. New York: Wiley, 1980.
28. L. Schweibert and L. Y. Wang, "Robust control and rate coordination for efficiency and fairness in ABR traffic with explicit rate marking," Comput. Commun., vol. 24, pp. 1329-1340, 2001.
29. J. F. Traub, G. W. Wasilkowski, and H. Wozniakowski, Information-Based Complexity. New York: Academic, 1988.
30. D. C. N. Tse, M. A. Dahleh, and J. N. Tsitsiklis, "Optimal asymptotic identification under bounded disturbances," IEEE Trans. Automat. Contr., vol. 38, pp. 1176-1190, Aug. 1993.
31. S. R. Venkatesh and M. A. Dahleh, "Identification in the presence of classes of unmodeled dynamics and noise," IEEE Trans. Automat. Contr., vol. 42, pp. 1620-1635, Dec. 1997.
32. V. N. Vapnik, Statistical Learning Theory. New York: Wiley, 1998.
33. _, The Nature of Statistical Learning Theory, 2nd ed, ser. Statistics for Engineering and Information Science. New York: Springer-Verlag, 1999.
34. M. Vidyasagar, Learning and Generalization: With Applications to Neural Networks, 2nd ed. New York: Springer-Verlag, 2003.
35. L. Y. Wang, "Persistent identification of time varying systems," IEEE Trans. Automat. Contr., vol. 42, pp. 66-82, Jan. 1997.
36. L. Y. Wang, I. V. Kolmanovsky, and J. Sun, "On-line identification lean NOx trap in GDI engines," presented at the 2000 Amer. Control Conf., Chicago, IL, June 2000.
37. L. Y. Wang, Y. Kim, and J. Sun, "Prediction of oxygen storage capacity and stored NOx using HEGO sensor model for improved LNT control strategies," presented at the 2002 ASME Int. Mechanical Engineering Congr. Exposition, New Orleans, LA, Nov. 17-22, 2002.
38. L. Y. Wang and L. Lin, "On metric dimensions of discrete-time systems," Syst. Control Lett., vol. 19, pp. 287-291, 1992.
39. L. Y. Wang and G. Yin, "Toward a harmonic blending of deterministic and stochastic frameworks in information processing," in Robustness in Identification and Control. New York: Springer-Verlag, 1999, LNCS, pp. 102-116.
40. _, "Persistent identification of systems with unmodeled dynamics and exogenous disturbances," IEEE Trans. Automat. Contr., vol. 45, pp. 1246-1256, July 2000.
41. _, "Closed-loop persistent identification of linear systems with unmodeled dynamics and stochastic disturbances," Automatica, vol. 38, no. 9, 2002.
42. G. Yin, V. Krishnamurthy, and C. Ion, "Iterate-averaging sign algorithms for adaptive filtering with applications to blind multiuser detection," IEEE Trans. Inform. Theory, vol. 49, pp. 657-671, Mar. 2003.
43. G. Zames, "On the metric complexity of causal linear systems e-entropy and ε-dimension for continuous time," IEEE Trans. Automat. Contr., vol. AC-24, pp. 222-230, Apr. 1979.
44. G. Zames, L. Lin, and L. Y. Wang, "Fast identification n-widths and uncertainty principles for LTI and slowly varying systems," IEEE Trans. Automat. Contr., vol. 39, pp. 1827-1838, Sept. 1994.