Title :
Global total least squares modeling of multivariable time series
Author :
Roorda, Berend ; Heij, Christiaan
Author_Institution :
Tinbergen Inst., Erasmus Univ., Rotterdam, Netherlands
fDate :
1/1/1995 12:00:00 AM
Abstract :
Presents a novel approach for the modeling of multivariable time series. The model class consists of linear systems, i.e., the solution sets of linear difference equations. Restricting the model order, the aim is to determine a model with minimal l2-distance from the observed time series. Necessary conditions for optimality are described in terms of state-space representations. These conditions motivate a relatively simple iterative algorithm for the nonlinear problem of identifying optimal models. Attractive aspects of the proposed method are that the model error is measured globally, it can be applied for multi-input, multi-output systems, and no prior distinction between inputs and outputs is required. The authors give an illustration by means of some numerical simulations
Keywords :
MIMO systems; difference equations; identification; least squares approximations; linear systems; modelling; state-space methods; time series; global total least squares modeling; iterative algorithm; linear difference equations; linear systems; multi-input multi-output systems; multivariable time series; necessary optimality conditions; numerical simulations; state-space representations; Delta modulation; Difference equations; Econometrics; Helium; Least squares methods; Linear systems; Numerical simulation; Polynomials; Stochastic systems; System identification;
Journal_Title :
Automatic Control, IEEE Transactions on