CONDITIONAL MAXIMUM LIKELIHOOD IDENTIFICATION FOR STATE SPACE SYSTEM, 1-8.

Luo Xiao, Harutoshi Ogai, Wang Jianhong, and Ricardo A. Ramirez Mendoza

References

  1. [1] L. Ljung System identification: Theory for user (Upper SaddleRiver, NJ: Prentice Hall, 1999).
  2. [2] S. Boyd and L. Vandenberghe Convex optimization (Cambridge: Cambridge University Press, 2004).
  3. [3] R. Pintelon and J. Schoukens System identification: A frequency domain approach (New York: IEEE Press, 2001).
  4. [4] A. Hagenblad, L. Ljung, and A. Wills, Maximum likelihoodidentification of Wiener models, Automatica, 44, 2008, 2697–2705.
  5. [5] F. Gustafsson and R. Karlsson, Generating dithering noise formaximum likelihood estimation from quantized data, Automatica, 49, 2013, 554–560.
  6. [6] J.C. Aguero and J.I. Yuz, On the equivalence of time andfrequency domain maximum likelihood estimation, Automatica,46, 2010, 260–270.
  7. [7] M.S. Aslam, Maximum likelihood least squares identificationmethod for active noise control systems with autoregressivemoving average noise, Automatica, 69, 2016, 1–11.
  8. [8] T. Soderstrom and U. Soverini, Errors in variables identificationusing maximum likelihood estimation in the frequency domain,Automatica, 79, 2017, 131–143.
  9. [9] L.B. White and H.X. Vu, Maximum likelihood sequence estimation for Hidder reciprocal process, IEEE Transactions onAutomatic Control, 58, 2013, 2670–2674.
  10. [10] R.J. Elliott, Filtering with uncertain noise, IEEE Transactionson Automatic Control, 62, 2017, 876–881.
  11. [11] T.T. Georgiou and A. Lindquist, Likelihood analysis of powerspectra and generalized moment problems, IEEE Transactionson Automatic Control, 62, 2017, 4580–4592.
  12. [12] R. Hostettler and W. Brirk, Maximum likelihood estimationof the non-parametric FRF for pulse like excitations, IEEETransactions on Automatic Control, 61, 2016, 2276–2281.

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