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Identification of Errors-in-Variables Systems with General Nonlinear Output Observations and with ARMA Observation Noises

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Abstract

This paper concerns the identification problem of scalar errors-in-variables (EIV) systems with general nonlinear output observations and ARMA observation noises. Under independent and identically distributed (i.i.d.) Gaussian inputs with unknown variance, recursive algorithms for estimating the parameters of the EIV systems are presented. For general nonlinear observations, conditions on the system are imposed to guarantee the almost sure convergence of the estimates. A simulation example is included to justify the theoretical results.

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Authors and Affiliations

  1. School of Mathematics, Renmin University of China, Beijing, 100872, China

    Qijiang Song & Zhiyong Huang

Authors
  1. Qijiang Song
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  2. Zhiyong Huang
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Corresponding author

Correspondence to Zhiyong Huang.

Additional information

This research was supported by the National Natural Science Foundation of China under Grant No. 11571362.

This paper was recommended for publication by Editor HU Xiaoming.

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Song, Q., Huang, Z. Identification of Errors-in-Variables Systems with General Nonlinear Output Observations and with ARMA Observation Noises. J Syst Sci Complex 33, 1–14 (2020). https://doi.org/10.1007/s11424-020-9009-z

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  • DOI: https://doi.org/10.1007/s11424-020-9009-z

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