Abstract
We consider multivariate time series which can be written as the output of a nonlinear system corrupted by noise. We propose an algorithm which allows to identify the structure of such a system and which generalizes a popular procedure for the identification of linear systems.
Similar content being viewed by others
References
Draper NR, Smith H (1981) Applied regression analysis. Wiley, New York
Fan J, Yao Q (2004) Nonlinear time series. Springer, Berlin Heidelberg New York
Guidorzi RP (1981) Invariants and canonical forms for systems structural and parametric identification. Automatica 17:117–133
Guidorzi RP, Losito M, Muratori T (1982) The range error test in the structural identification of linear multivariable systems. IEEE Trans Autom Control 27:1044–1054
Nelles O (2001) Nonlinear system identification. Springer, Berlin Heidelberg New York
Pötscher B, Prucha I (1997) Dynamic nonlinear econometric models. Springer, Berlin Heidelberg New York
Stoica P (1984) Comments on The range error test in the structural identification of linear multivariable systems. IEEE Trans Autom Control 17:117–133
Acknowledgment
We thank both referees for their detailed and constructive comments which led to a considerable improvement of the paper.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Franke, J., Löhr, J. On the identification of large multilinear systems. Computational Statistics 21, 415–429 (2006). https://doi.org/10.1007/s00180-006-0003-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00180-006-0003-2