Recursive identification for multivariate errors-in-variables systems

The recursive algorithm is given for estimating matrix coefficients of the multivariate errors-in-variables (EIV) systems. It is shown that under mild conditions the estimate given by the algorithm converges to a limit belonging to the solution set of the Yule-Walker equation satisfied by the true coefficients of the system. The sufficient conditions guaranteeing the uniqueness of the solution to the Yule-Walker equation are given. In this case the estimate provided by the recursive algorithm is strongly consistent.

[1]  Lei Guo,et al.  Least-squares identification for ARMAX models without the positive real condition , 1989 .

[2]  Torsten Söderström,et al.  Perspectives on errors-in-variables estimation for dynamic systems , 2002, Signal Process..

[3]  A. M. Walker Large-sample estimation of parameters for movingaverage models , 1961 .

[4]  Han-Fu Chen,et al.  A Kiefer-Wolfowitz algorithm with randomized differences , 1999, IEEE Trans. Autom. Control..

[5]  A. M. Walker Large-sample estimation of parameters for autoregressive processes with moving-average residuals , 1962 .

[6]  Han-Fu Chen,et al.  Strongly consistent coefficient estimate for errors-in-variables models , 2005, Autom..

[7]  Xiao-Li Hu,et al.  Strong consistence of recursive identification for Wiener systems , 2005, Autom..

[8]  Han-Fu Chen,et al.  Strong consistency of recursive identification for Hammerstein systems with discontinuous piecewise-linear memoryless block , 2005, IEEE Transactions on Automatic Control.

[9]  Han-Fu Chen Stochastic approximation and its applications , 2002 .

[10]  D. Lainiotis,et al.  System identification : advances and case studies , 1976 .

[11]  Petre Stoica,et al.  On the uniqueness of prediction error models for systems with noisy input-output data , 1987, Autom..

[12]  Gang George Yin,et al.  Asymptotic properties of sign algorithms for adaptive filtering , 2003, IEEE Trans. Autom. Control..

[13]  Petre Stoica,et al.  Generalized Yule-Walker equations and testing the orders of multivariate time series , 1983 .

[14]  Jitendra Tugnait Stochastic system identification with noisy input using cumulant statistics , 1992 .

[15]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[16]  Jonathan D. Cryer,et al.  Time Series Analysis , 1986, Encyclopedia of Big Data.

[17]  A. Toola,et al.  The safety of process automation , 1993, Autom..

[18]  Han-Fu Chen,et al.  Identification and Stochastic Adaptive Control , 1991 .

[19]  Han-Fu Chen,et al.  Pathwise convergence of recursive identification algorithms for Hammerstein systems , 2004, IEEE Transactions on Automatic Control.

[20]  B. Anderson,et al.  Optimal Filtering , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[21]  Torsten Söderström,et al.  Identification of dynamic errors-in-variables models: Approaches based on two-dimensional ARMA modeling of the data , 2003, Autom..

[22]  Han-Fu Chen,et al.  Stability and instability of limit points for stochastic approximation algorithms , 2000, IEEE Trans. Autom. Control..

[23]  Umberto Soverini,et al.  Identification of dynamic errors-in-variables models , 1996, Autom..

[24]  B. Anderson,et al.  Identifiability in dynamic errors-in-variables models , 1983, The 22nd IEEE Conference on Decision and Control.

[25]  T. Söderström,et al.  Identification of dynamic errors-in-variables model using a frequency domain Frisch scheme , 2002 .

[26]  Brian D. O. Anderson,et al.  Identification of scalar errors-in-variables models with dynamics , 1985, Autom..

[27]  Michel Loève,et al.  Probability Theory I , 1977 .

[28]  Manfred Deistler,et al.  A Structure Theory for Linear Dynamic Errors-in-Variables Models , 1998 .

[29]  Jitendra K. Tugnait,et al.  Stochastic system identification with noisy input using cumulant statistics , 1990, 29th IEEE Conference on Decision and Control.