Box-Jenkins continuous-time modeling

This paper treats the identification of continuous-time models using arbitrary band-limited excitation signals. A modeling approach is presented that has the following two advantages: (1)asymptotically (the amount of data tends to infinity) there is no approximation error over the complete frequency band from DC to Nyquist, (2)it allows to identify general parametric noise models. The key idea is to combine a continuous-time plant model with a discrete-time noise model (=hybrid Box-Jenkins model structure).

[1]  D. Brillinger.  Time series - data analysis and theory , 1981, Classics in applied mathematics.

[2]  Rik Pintelon,et al.  Identification of Continuous-Time Systems Using Arbitrary Signals , 1997 .

[3]  Michel Verhaegen,et al.  Continuous-time identification of SISO systems using Laguerre functions , 1999, IEEE Trans. Signal Process..

[4]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[5]  Rolf Johansson,et al.  Identification of continuous-time models , 1994, IEEE Trans. Signal Process..

[6]  Rik Pintelon,et al.  Discrete-time modeling and identification of continuous-time systems: a general framework , 1991 .

[7]  N. Sinha,et al.  Identification of Continuous-Time Systems: Methodology and Computer Implementation , 1991 .

[8]  J. Schoukens,et al.  Parametric and nonparametric identification of linear systems in the presence of nonlinear distortions-a frequency domain approach , 1998, IEEE Trans. Autom. Control..

[9]  J. Schoukens,et al.  Frequency domain system identification using arbitrary signals , 1997, IEEE Trans. Autom. Control..

[10]  Gerd Vandersteen,et al.  Frequency-domain system identification using non-parametric noise models estimated from a small number of data sets , 1997, Autom..

[11]  Malvin Carl Teich,et al.  Power-law shot noise , 1990, IEEE Trans. Inf. Theory.

[12]  Thomas Kailath,et al.  Linear Systems , 1980 .

[13]  H. Unbehauen,et al.  Identification of continuous-time systems , 1991 .

[14]  T. Söderström.  Convergence properties of the generalised least squares identitication method , 1974, Autom..

[15]  Soederstroem.  ON THE CONVERGENCE PROPERTIES OF THE GENERALIZED LEAST SQUARES IDENTIFICATION METHOD. , 1972 .

[16]  V. P. Pyatti.  An exact expression for the noise voltage across a resistor shunted by a capacitor , 1992 .

[17]  Graham C. Goodwin,et al.  Digital control and estimation : a unified approach , 1990 .

[18]  Rik Pintelon,et al.  Time series analysis in the frequency domain , 1999, IEEE Trans. Signal Process..

[19]  Rik Pintelon,et al.  Identification of Linear Systems: A Practical Guideline to Accurate Modeling , 1991 .

[20]  J. Schoukens,et al.  Parametric identification of transfer functions in the frequency domain-a survey , 1994, IEEE Trans. Autom. Control..

[21]  Eugene Lukacs.  STOCHASTIC CONVERGENCE CONCEPTS AND THEIR PROPERTIES , 1975 .

[22]  Michel Verhaegen,et al.  Stochastic theory of continuous-time state-space identification , 1999, IEEE Trans. Signal Process..

[23]  J. Schoukens,et al.  Identification of linear systems in the presence of nonlinear distortions. A frequency domain approach. I. Non-parametric identification , 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.