Stochastic subspace identification guaranteeing stability and minimum phase

IFAC Proceedings Volumes (IFAC-PapersOnline)

Volumn 16, Issue , 2005, Pages 910-915

  Tanaka, Hideyuki ^a   Katayama, Tohru ^a  

^a KYOTO UNIVERSITY   (Japan)

Author keywords

Balanced realization; Canonical correlation analysis; LQ decomposition; Spectral factorization technique; Stochastic realization; Subspace identification

Indexed keywords

FACTORIZATION; MARKOV PROCESSES; RICCATI EQUATIONS; STOCHASTIC MODELS;

BALANCED REALIZATIONS; CANONICAL CORRELATION ANALYSIS; LQ DECOMPOSITION; SPECTRAL FACTORIZATIONS; STOCHASTIC REALIZATION; SUBSPACE IDENTIFICATION;

STOCHASTIC SYSTEMS;

EID: 79960742465     PISSN: 14746670     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3182/20050703-6-cz-1902.00153     Document Type: Conference Paper

References (16)

1. Aoki, M. (1990). State Space Modeling of Time Series, 2nd Ed., Springer.
2. Arnold, W. F. and A. J. Laub (1984). Generalized eigenproblem algorithms and software for algebraic Riccati equations. Proc. of the IEEE 72(12), 1746-1754.
3. Desai, U. B., D. Pal and R. D. Kirkpatrick (1985). A realization approach to stochastic model reduction. Int. J. Control 42(4), 821-838.
4. Faurre, P. L. (1976). Stochastic realization algorithms. In: System Identification: Advances and Case Studies (R. Mehra and D. G. Lainiotis, Eds.). pp. 1-25. Academic Press.
5. Goethals, I., T. Van Gestel, J. Suykens, P. Van Dooren and B. De Moor (2003). Identification of positive real models in subspace identification by using regularization. IEEE Trans. on Automat. Contr. 48(10), 1843-1847.
6. Lindquist, A. and G. Picci (1996a). Canonical correlation analysis, approximate covariance extension, and identification of stationary time series. Automatica 32(5), 709-733.
7. Lindquist, A. and G. Picci (1996b). Geometric methods for state space identification. In: Identification, Adaptation, Learning (S. Bittanti and G. Picci, Eds.). pp. 1-69. Springer-Verlag.
8. Maciejowski, J. M. (1996). Parameter estimation of multivariable systems using balanced realizations. In: Identification, Adaptation, Learning (S. Bittanti and G. Picci, Eds.). pp. 70-119. Springer-Verlag.
9. Mari, J., P. Stoica and T. McKelvey (2000). Vector ARMA estimation: A reliable subspace approach. IEEE Transactions on Signal Processing 48(7), 2092-2104.
10. Tanaka, H. and T. Katayama (2003a). A stochastic realization in a Hilbert space based on "LQ decomposition". Proc. of the 13th IFAC Symposium on System Identification (SYSID 2003) pp. 899-904.
11. Tanaka, H. and T. Katayama (2003b). Stochastic realization on a finite interval via "LQ Decomposition" in Hilbert space. Proc. of European Control Conference (ECC 2003).
12. Tanaka, H. and T. Katayama (2003c). Stochastic subspace identification via "LQ decomposition". Proc. of the 42nd IEEE Conference on Decision and Control (CDC 2003) pp. 3467-3472.
13. Tanaka, H. and T. Katayama (2004a). On a stochastic subspace identification algorithm via a block LQ decomposition. Proc. of SSS 2004, Tokyo Denki University, Saitama, Japan (to appear).
14. Tanaka, H. and T. Katayama (2004b). A stochastically balanced realization on a finite-interval. Proc. of the 16th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2004).
15. Van Overschee, P. and B. De Moor (1993). Subspace algorithms for the stochastic identification problem. Automatica 29(3), 649-660.
16. Van Overschee, P. and B. De Moor (1996). Subspace Identification for Linear Systems. Kluwer Academic Pub.