Abstract
This paper is concerned with the parameter estimation of deterministic autoregressive moving average (DARMA) systems with quantization data. The estimation algorithms adopted here are the least squares (LS) and the forgetting factor LS, and the signal quantizer is of uniform, that is, with uniform quantization error. The authors analyse the properties of the LS and the forgetting factor LS, and establish the boundedness of the estimation errors and a relationship of the estimation errors with the size of quantization error, which implies that the smaller the quantization error is, the smaller the estimation error is. A numerical example is given to demonstrate theorems.
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The work was supported by National Key R&D Program of China under Grant No. 2018YFA0703800; the National Natural Science Foundation of China under Grant No. 61877057.
This paper was recommended for publication by Editor WU Zhengguang.
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Jing, L., Zhang, JF. LS-Based Parameter Estimation of DARMA Systems with Uniformly Quantized Observations. J Syst Sci Complex 35, 748–765 (2022). https://doi.org/10.1007/s11424-021-0314-y
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DOI: https://doi.org/10.1007/s11424-021-0314-y