Bayesian method for identification of constrained nonlinear processes with missing output data

Proceedings of the American Control Conference

Volumn , Issue , 2011, Pages 96-101

  Deng, Jing ^a   Huang, Biao ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

Constrained particle filters; Expectation Maximization method; Missing outputs; Nonlinear state space model; Parameter estimation

Indexed keywords

BAYESIAN METHODS; EM ALGORITHMS; EXPECTATION-MAXIMIZATION ALGORITHMS; EXPECTATION-MAXIMIZATION METHOD; EXPECTED VALUES; INCOMPLETE DATA; LOG-LIKELIHOOD FUNCTIONS; MISSING DATA; MISSING OBSERVATIONS; MISSING OUTPUTS; NON-LINEAR MODEL; NONLINEAR PROCESS; NONLINEAR STATE SPACE MODELS; NUMERICAL EXAMPLE; OUTPUT DATA; PARTICLE FILTER;

BAYESIAN NETWORKS; CHEMICAL INDUSTRY; DATA HANDLING; IDENTIFICATION (CONTROL SYSTEMS); MAXIMUM PRINCIPLE; NONLINEAR FILTERING; NUMERICAL METHODS; PARAMETER ESTIMATION; STATE SPACE METHODS;

ALGORITHMS;

EID: 80053146962     PISSN: 07431619     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. P. K. Pearson, Control Systems, Identification, Encyclopedia of Physical Science and Technology, 2004, pp 687-707.
2. B. F. Feeny, NONLINEAR SYSTEM IDENTIFICATION, Encyclopedia of Vibration, 2004, pp 924-928.
3. S. Khatibisepehr and B. Huang, Dealing with Irregular Data in Soft Sensors: Bayesian Method and Comparative Study, Industrial & Engineering Chemistry Research, vol. 22, 2008, pp 8713-8723.
4. R. Wallin, A. J. Isaksson and L. Ljung, An iterative method for identification of ARX models from inceomplete data, Proceedings of the 39th IEEE Conference on Decision and Control, Sydney, Australia, December 2000.
5. A. P. Demspter, N. M. Larid and D. B. Rubin, Maximum Likelihood from Incomplete Data via the EM algorithm, Journal of the Royal Statistical Society, Series B, vol. 39, 1977, pp 1-38.
6. R. H. Shumway and D. S. Stoffer, An Approach to Time Series Smoothing and Forcasting Using the em Algorithm, Journal of Time Series Analysis, vol. 3, 1982, pp 253-264.
7. A. Doucet, N. De Freitas and N. Gordon, Sequential Monte Carlo Methods in Practice, Springer, New York, 2001.
8. R. B. Gopaluni, A Particle Filter Approach to Identification of Nonlinear Process under Missing Observations, The Canadian Journal of Chemical Engineering, vol. 86, 2008, pp 1081-1092.
9. J. Kim, Ph.D Thesis, University of Pittsburgh, Pittsburgh, 2005.
10. M. S. Arulampalam, S. Maskell, N. Gordon and T. Clapp, A Tutorial on Particle Filters for Onine Nonlinear/Non-Guassian Bayesian Tracking, IEEE Transaction on Signal Processing, vol. 50, 2002, pp 174-188. (Pubitemid 34291500)
11. N. Bergman, Recursive Bayesian estimation: Navigation and tracking applications, Ph.D Dissertation, Linkping Univ., Linkping, Sweden, 1999.
12. A. Doucet, On sequential Monte Carlo methods for Bayesian filtering, Dept. Eng., Univ. Cambridge, UK, Technical Report, 1998.
13. J. S. Liu and R. Chen, Sequential Monte Carlo methods for dynamical systems, Journal of the American Statistical Association, vol. 93, 1998, pp 1032-1044.
14. L. Lang, W. Chen, B. R. Bakshi, P. K. Goel and S. Ungarala, Bayesian estimation via sequential Monte Carlo sampling-Constrained dynamic systems, Automatica, vol. 43, 2007, pp 1615-1622. (Pubitemid 47198207)
15. X. Shao, B. Huang and J. M. Lee, Constrained Bayesian state estimation - A comparative study and a new particle filter based approach, Journal of Process Control, vol. 20, 2010, pp 143-157.
16. G. C. Goodwin and J. C. Aguero, Approximate EM algorithms for parameter and state estimation in nonlinear stochastic models, Proceedings of the 44th IEEE Conference on Decision and Control, Seville, Spain, 2005.
17. P. Vachhani, S. Narasimhan and R. Rengaswamy, Robust and reliable estimation via unscented recursive nonlinear dynamic data reconcillation, Journal of Process Control, vol. 16, 2006, pp 1075-1086. (Pubitemid 44635211)
18. S. Ungarala, K. Li and Z. Chen, Constrained Bayesian state estimation using a cell filter, Industrial & Engineering Chemistry Research, vol. 47, 2008, pp 7312-7322.
19. R. Senthil, K. Janarthanan, J. Prakash, Nonliear state estimation using fuzzy kalman filter, Industrial & Engineering Chemistry Research, vol. 25, 2006, pp 8678-8688. (Pubitemid 46057419)
20. Z. Xu, J. Zhao, J. Qian and Y. Zhu, Nonliear MPC using identified LPV model, Industrial & Engineering Chemistry Research, vol. 6, 2009, pp 3043-3051.