Fast numerical algorithms for wiener systems identification

IFIP Advances in Information and Communication Technology

Volumn 121, Issue , 2003, Pages 375-386

  Sima, Vasile ^a  

^a NATIONAL INSTITUTE FOR RESEARCH AND DEVELOPMENT IN INFORMATICS   (Romania)

Author keywords

Conjugate gradients; Least squares approximation; Levenberg Marquardt algorithm; Optimization; System identification

Indexed keywords

CONJUGATE GRADIENT METHOD; DIFFERENTIAL EQUATIONS; IDENTIFICATION (CONTROL SYSTEMS); JACOBIAN MATRICES; LEAST SQUARES APPROXIMATIONS; LINEAR SYSTEMS; MULTILAYER NEURAL NETWORKS; NETWORK LAYERS; OPTIMIZATION; STATE SPACE METHODS;

CONJUGATE GRADIENTS; FAST NUMERICAL ALGORITHM; LEVENBERG-MARQUARDT ALGORITHM; QR FACTORIZATIONS; STATE - SPACE MODELS; STATE SPACE REPRESENTATION; STATIC NON-LINEARITY; SUBSPACE IDENTIFICATION;

APPROXIMATION ALGORITHMS;

EID: 84904308885     PISSN: 18684238     EISSN: None     Source Type: Book Series    
DOI: None     Document Type: Conference Paper

References (15)

1. Anderson, E., Bai, Z., Bischof, C, Blackford, S., Demmel, J., Dongarra, J., Du Croz, J., Greenbaum, A., Hammarling, S., McKenney, A., and Sorensen, D. (1999). LAPACK Users' Guide: Third Edition. Software, Environments, Tools. SIAM, Philadelphia.
2. Delebecque, F. and Steer, S. (1997). Integrated Scientific Computing with Scilab. Birkhauser, Boston.
3. Golub, G. H. and Van Loan, C. F. (1996). Matrix Computations. M. D. Johns Hopkins University Press, Baltimore, Maryland, third edition.
4. Kelley, C. T. (1999). Iterative Methods for Optimization. SIAM, Philadelphia, PA.
5. Mastronardi, N., Kressner, D., Sima, V., Van Dooren, P., and Van Huffel, S. (2001). A fast algorithm for subspace state-space system identification via exploitation of the displacement structure. J. Comput. Appl. Math., 132(1):71-81.
6. More, J. J. (1978). The Levenberg-Marquardt algorithm: Implementation and theory. In Watson, G. A., editor, Numerical Analysis, volume 630 of Lecture Notes in Mathematics, pages 105-116. Springer-Verlag, Berlin, Heidelberg and New York.
7. More, J. J., Garbow, B. S., and Hillstrom, K. E. (1980). User's guide for MINPACK-1. Report ANL-80-74, Applied Math. Division, Argonne National Laboratory, Argonne, Illinois.
8. Peeters, R., Hanzon, B., and Olivi, M. (1999). Balanced realizations of discrete-time stable all-pass systems and the tangential Schur algorithm. In Proceedings of the European Control Conference 31 August-3 September 1999, Karlsruhe, Germany. Session CP-6, Discrete-time Systems.
9. Press, W. H., Teukolsky, S. A., Wetterling, W. T., and Flannery, B. P. (1992). Numerical Recipes. The Art of Scientific Computing. Cambridge University Press, New York, second edition.
10. Schneider, R., Riedel, A., Verdult, V., Verhaegen, M., and Sima, V. (2002). SLICOT system identification toolbox for nonlinear Wiener systems. SLICOT Working Note 2002-6, Katholieke Universiteit Leuven (ES AT/SISTA), Leuven, Belgium. Available from http://wgs.esat.kuleuven.ac.be/pub/WGS/REP0RTS/SLWN2002-6.ps.Z.
11. Sima, V. (1999). Cholesky or QR factorization for data compression in subspace-based identification? In Proceedings of the Second NICONET Workshop on Numerical Control Software: SLICOT, a Useful Tool in Industry, December 3, 1999, INRIA Rocquencourt, France, pages 75-80.
12. Sima, V. and Van Huffel, S. (2001). Performance Investigation of SLICOT System Identification Toolbox, In Proceedings of the European Control Conference, ECC 2001, J-l September, 2001, Semindrio de Vilar, Porto, Portugal, pages 3586-3591.
13. Verhaegen, M. (1993). Subspace model identification. Part 3: Analysis of the ordinary output-error state-space model identification algorithm. Int. J. Control, 58(3):555-586.
14. Verhaegen, M. (1998). Identification of the temperature-product quality relationship in a multi-component distillation column. Chemical Engineering Communications, 163:111-132.
15. Westwick, D. and Verhaegen, M. (1996). Identifying MIMO Wiener systems using subspace model identification methods. Signal Processing, 52:235-258.