Principles of System Identification

References

Abdi, H. (2010). Partial least squares regression and projection on latent structure regression (PLS

Regression). Wiley Interdisciplinary Reviews: Computational Statistics, 2 (1), pp. 97–106.

Abdulle, A. and G. Wanner (2002). 200 years of the least squares method. Elemente der Mathematik,

57, pp. 45–60.

Addison, P. (2002). The Illustrated Wavelet Transform Handbook: Introductory Theory and Appli-

cations in Science, Engineering, Medicine and Finance. London, UK: Institute of Physics.

Akaike, H. (1969). Fitting autoregressive models for prediction. Annals of the Institute of Statistics

and Mathematics, 21, pp. 243–347.

– (1973). Information theory and an extension of the maximum likelihood principle. In: Second

International Symposium on Information Theory. Budapest, Hungary, pp. 267–281.

– (1974a). A new look at the statistical model identification. IEEE Transactions on Automatic

Control, AC-19, pp. 716–723.

– (1974b). Stochastic theory of minimal realization. IEEE Transactions on Automatic Control, 19,

pp. 667–674.

Albertos, P. and A. Sala, eds. (2002). Iterative Identification and Control. London, UK: Springer-

Verlag.

Aldrich, J. (1997). R. A. Fisher and the making of maximum likelihood: 1912-1922. Statistical

Science, 12 (3), pp. 162–176.

Amemiya, T. (1985). Advanced Econometrics. Harvard University Press.

Anderson, T. (1971). The Statistical Analysis of Time Series. John Wiley & Sons, Inc.

Angrist, J. D. and A. B. Krueger (2001). Instrumental variables and the search for identification:

from supply and demand to natural experiments. Journal of Economic Perspectives, 15 (4),

pp. 69–85.

Antoniou, A. (2006). Digital Signal Processing: Signals Systems and Filters. USA: McGraw-Hill.

Arun, K. and S. Kung (1990). Balanced approximation of stochastic systems. SIAM Journal of

Matrix Analysis and Applications, 11, pp. 42–68.

Åström, K. J. and B. Wittenmark (1997). Computer-Controlled Systems: Theory and Design. 3

rd

edition. Englewood Cliffs, NJ, USA: Prentice Hall.

Åström, K. and T. Bohlin (1965). Numerical identification of linear dynamic systems from normal

operating records. In: IFAC Symposium on Self-Adaptive Systems. Teddington, UK, pp. 96–111.

Åström, K. and P. Eykhoff (1971). System identification - a survey. Automatica, 7, pp. 123–162.

Åström, K. and B. Wittenmark (1989). Adaptive Control. Reading, MA: Addison-Wesley.

Babji, S. and A. K. Tangirala (2009). Time-delay estimation in Closed-Loop Processes using Aver-

age Mutual Information Theory. Control and Intelligent Systems, 37 (3), pp. 176–182.

Baccala, L. and K. Sameshima (2001). Partial directed coherence: a new concept in neural structure

determination. Biological Cybernetics, 84, pp. 463–474.

Badwe, A., R. Gudi, R. Patwardhan, S. Shah and S. Patwardhan (2009). Detection of model-plant

mismatch in MPC applications. Journal of Process Control, 19, pp. 1305–1313.

Badwe, A., R. Patwardhan, S. Shah, S. Patwardhan and R. Gudi (2010). Quantifying the impact of

model-plant mismatch on controller performance. Journal of Process Control, 20, pp. 408–425.

Bakshi, A., A. Koulanis and G. Stephanopoulos (1994). Wave-nets: novel learning techniques, and

the induction of physically interpretable models. In: SPIE, pp. 637–648.

Baldacchino, T., S. R. Anderson and V. Kadirkamanathan (2013). Computational system identifica-

tion for Bayesian NARMAX modelling. Automatica, 49 (9), pp. 2641–2651.

Barenthin, M. (2006). “On input design in system identification for control”. PhD thesis. Stockholm,

Sweden: KTH School of Electrical Engineering.

814