Erratum to: Identification of Wiener systems with binary-valued output observations [Automatica 43 (2007) 1752-1765] (DOI:10.1016/j.automatica.2007.03.006);Identification of Wiener systems with binary-valued output observations

Automatica

Volumn 43, Issue 10, 2007, Pages 1752-1765

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

^a CHINESE ACADEMY OF SCIENCES   (China)

^b Wayne State University   (United States)

^c WAYNE STATE UNIVERSITY   (United States)

Author keywords

Binary valued observations; Identification; Joint identifiability; Parameter estimation; Periodic inputs; Sensor thresholds; Wiener systems

Indexed keywords

ALGORITHMS; ASYMPTOTIC STABILITY; DISTRIBUTION FUNCTIONS; SENSORS;

BINARY-VALUED OBSERVATIONS; JOINT IDENTIFIABILITY; PERIODIC INPUTS; SENSOR THRESHOLDS; WIENER SYSTEMS;

IDENTIFICATION (CONTROL SYSTEMS);

EID: 34548499638     PISSN: 00051098     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.automatica.2007.10.002     Document Type: Erratum

References (35)

1. Bai E.W. An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear system. Automatica 34 (1998) 333-338
2. Bai E.W. Frequency domain identification of Wiener models. Automatica 39 (2003) 1521-1530
3. Billingsley P. Convergence of probability measures (1968), Wiley, New York
4. Billings S. Identification of nonlinear systems-a survey. Proceedings of IEE, Part D 127 (1980) 272-285
5. Celka P., Bershad N.J., and Vesin J.M. Stochastic gradient identification of polynomial Wiener systems: Analysis and application. IEEE Transactions on Signal Processing 49 (2001) 301-313
6. Chen H.F. Recursive identification for Wiener model with discontinuous piece-wise linear function. IEEE Transactions on Automatic Control 51 (2006) 390-400
7. Chen H.F., and Guo L. Identification and stochastic adaptive control (1991), Birkhäuser, Boston
8. Feller, W. (1968). An introduction to probability theory and its applications (Vol. I, 3rd ed.). New York: Wiley.
9. Feller, W. (1971). An introduction to probability theory and its applications (Vol. II, 2nd ed.). New York: Wiley.
10. Horn R.A., and Johnson C.R. Matrix analysis (1985), Cambridge University Press, Cambridge
11. Hu X.L., and Chen H.F. Strong consistence of recursive identification for Wiener systems. Automatica 41 (2005) 1095-1916
12. Hunter I.W., and Korenberg M.J. The identification of nonlinear biological systems: Wiener and Hammerstein cascade models. Biological Cybernetics 55 (1986) 135-144
13. Korenberg M.J., and Hunter I.W. Two methods for identifying Wiener cascades having noninvertible static nonlinearities. Annals of Biomedical Engineering 27 (1998) 793-804
14. Krishnamarty V. Estimation of quantized linear errors-in-variables models. Automatica 31 (1995) 1459-1464
15. Kushner H.J., and Yin G. Stochastic approximation and recursive algorithms and applications. (2nd ed.) (2003), Springer, New York
16. Lacy, S. L., & Bernstein, D. S. (2002). Identification of FIR Wiener systems with unknown, noninvertible polynomial nonlinearities. In Proceedings of the American Control Conference (pp. 893-899).
17. Lancaster P., and Tismenetsky M. The theory of matrices. (2nd ed.) (1985), Academic Press, New York
18. Ninness B., and Gibson S. Quantifying the accuracy of Hammerstein model estimation. Automatica 38 (2002) 2037-2051
19. Norquay S.J., Palazoglu A., and Romagnoli J.A. Application of Wiener model predictive control (WMPC) to pH neutralization experiment. IEEE Transactions on Control Systems Technology 7 (1999) 437-445
20. Roll J., Nazin A., and Ljung L. Nonlinear system identification via direct weight optimization. Automatica 41 (2005) 475-490
21. Royden H.L. Real analysis. (3rd ed.) (1988), Macmillan, New York
22. Schoukens J., Nemeth J.G., Crama P., Rolain Y., and Pintelon R. Fast approximate identification of nonlinear systems. Automatica 39 (2003) 1267-1274
23. Sjoberg J., Zhang Q., Ljung L., Benveniste A., Delyon B., Glorennec P., et al. Nonlinear black-box modeling in system identification: A unified overview. Automatica 31 (1995) 1691-1724
24. Verhaegen M., and Westwick D. Identifying MIMO Wiener systems using subspace model identification methods. Signal Processing 52 (1996) 235-258
25. Wigren T. Convergence analysis of recursive identification algorithms based on the nonlinear Wiener model. IEEE Transactions on Automatic Control 39 (1994) 2191-2206
26. Wigren T. Approximate gradients, convergence and positive realness in recursive identification of a class of nonlinear systems. International Journal of Adaptive Control and Signal Processing 9 (1995) 325-354
27. Wigren T. Adaptive filtering using quantized output measurements. IEEE Transactions on Signal Processing 46 (1998) 3423-3426
28. Wang, L. Y., Kim, Y., & Sun, J. (2002). Prediction of oxygen storage capacity and stored NOx using HEGO sensor model for improved LNT control strategies. In 2002 ASME international mechanical engineering congress and exposition. New Orleans, November 17-22.
29. Wang, L. Y., & Wang, H. (2002). Control-oriented modeling of BIS-based patient response to anesthesia infusion. In 2002 international conference on mathematical engineering techniques in medicine and biological sciences. Las Vegas, June 24-27.
30. Wang, L. Y., & Yin, G. (2007). Asymptotically efficient parameter estimation using quantized output observations Automatica, 43, 1178-1191.
31. Wang L.Y., Yin G., and Wang H. Identification of Wiener models with anesthesia applications. International Journal Pure & Applied Science 1 (2004) 35-61
32. Wang L.Y., Yin G., and Zhang J.F. Joint identification of plant rational models and noise distribution functions using binary-valued observations. Automatica 42 (2006) 535-547
33. Wang, L. Y., Yin, G., & Zhang, J. F. (2006b). Space and time complexities, sensor threshold selection, and input design of quantized identification, submitted for publication.
34. Wang L.Y., Zhang J.F., and Yin G. System identification using binary sensors. IEEE Transactions on Automatic Control 48 (2003) 1892-1907
35. Yin G., Kan S., and Wang L.Y. Identification error bounds and asymptotic distributions for systems with structural uncertainties. Journal of Systems Science and Complexity 19 (2006) 22-35