Selection of parameter sets and design of experiments for estimation of nonlinear dynamic systems

IFAC Proceedings Volumes (IFAC-PapersOnline)

Volumn 17, Issue 1 PART 1, 2008, Pages

  Chu, Yunfei ^a   Hahn, Juergen ^a  

^a NONE

Author keywords

Process modeling and identification

Indexed keywords

COMPLEX PROCESSES; EXPERIMENTAL CONDITIONS; EXPERIMENTAL DESIGN; INITIAL STATE; INPUT SIGNAL; MEASUREMENT DATA; NON-LINEAR DYNAMIC SYSTEMS; NONLINEAR BEHAVIOR; OPTIMAL SETS; OPTIMIZATION PROBLEMS; PARAMETER SELECTION; PARAMETER SET; PARAMETER VALUES; PROCESS MODELING AND IDENTIFICATION; SENSITIVITY VALUES;

DESIGN OF EXPERIMENTS; DYNAMICAL SYSTEMS; MATHEMATICAL MODELS; SENSITIVITY ANALYSIS;

PARAMETER ESTIMATION;

EID: 79961019220     PISSN: 14746670     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3182/20080706-5-KR-1001.1093     Document Type: Conference Paper

References (18)

1. Brun, R., P. Reichert and H.R. Kunsch (2001). Practical identifiability analysis of large environmental simulation models. Water Resour. Res., 37, pp. 1015-1030.
2. Brun, R., M. Kuhni, H. Siegrist, W. Gujer and P. Reichert (2002). Practical identifiability of ASM2d parameters - systematic selection and tuning of parameter subsets. Water Res., 38, pp. 4113-4127. (Pubitemid 35303342)
3. Chu, Y. and J. Hahn (2007). Parameter set selection for estimation for nonlinear dynamic systems. AIChE J., 53, pp. 2858-2870. (Pubitemid 350074579)
4. Kiefer, J. (1959). Optimum experimental designs. J. R. Stat. Soc. Ser. B-Stat. Methodol., 21, pp. 272-319.
5. Li, R.J., M.A. Henson and M.J. Kurtz (2004). Selection of model parameters for off-line parameter estimation. IEEE Trans. Control Syst. Technol., 12, pp. 402-412.
6. Ljung, L. (1999). System Identification: Theory for the User. 2nd ed. Prentice Hall PTR, Upper Saddle River, NJ.
7. Muske, K.R. and C. Georgakis (2003). Optimal measurement system design for chemical processes. AIChE J., 49, pp. 1488-1494. (Pubitemid 36712609)
8. Nelles, O. (2001). Nonlinear System Identification: from Classical Approaches to Neural Networks and Fuzzy Models. Springer, Berlin.
9. Pukelsheim, F. (1993). Optimal design of experiments. Wiley, New York.
10. Robbins, H. and S. Monro (1951). A stochastic approximation method. Ann. Math. Stat., 22, pp. 400-407.
11. Sandink, C.A., K.B. McAuley and P.J. McLellan (2001). Selection of parameters for updating in on-line models Ind. Eng. Chem. Res., 40, pp. 3936-3950.
12. Silvey, S.D. (1980). Optimal design: an introduction to the theory for parameter estimation. Chapman and Hall, London.
13. Spall, J.C. (1992). Multivariate stochastic-approximation using a simultaneous perturbation gradient approximation. IEEE Trans. Autom. Control, 37, pp. 332-341.
14. Spall, J.C. (1998). Implementation of the simultaneous perturbation algorithm for stochastic optimization. IEEE Trans. Aerosp. Electron. Syst., 34, pp. 817-823.
15. Walter, E. and L. Pronzato (1990). Qualitative and quantitative experiment design for phenomenological models - A survey. Automatica, 26, pp. 195-213.
16. Weijers, S.R. and P.A. Vanrolleghem (1997). A procedure for selecting best identifiable parameters in calibrating activated sludge model no.1 to full-scale plant data. Water Sci. and Tech., 36, pp. 69-79.
17. Velez-Reyes, M. and G.C. Verghese (1995). Subset selection in identification, and application to speed and parameter estimation for induction machines. In Proc. 4th IEEE Conf. on Control Applications, Albany, pp. 991-997.
18. Yao, K.Z., B.M. Shaw, B. Kou, K.B. McAuley and D.W. Bacon (2003). Modeling ethylene/butene copolymerization with multi-site catalysts: Parameter estimability and experimental design. Poly. Reac. Eng., 11, pp. 563-588.