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- Title
Parameter estimation of quantized DARMA systems using weighted least squares.
- Authors
Jing, Lida
- Abstract
This paper is concerned with parameter estimate of deterministic autoregressive moving average (DARMA) systems with uniform quantized output observations. By designing system input signals, the recursive least‐squares algorithm with designed weights is proved to have convergence properties under the uniform output signal quantizer. The authors analyse the properties of the size of quantization error, which implies that the convergence properties can be achieved when the quantization error satisfies some conditions. A numerical example is supplied to demonstrate the theoretical results. This paper researches the parameter estimate of deterministic autoregressive moving average systems with quantized output data. A recursive least‐squares algorithm with designed weights is proved to have convergence properties under the uniform signal quantizer.
- Subjects
PARAMETER estimation; LEAST squares; MOVING average process
- Publication
IET Control Theory & Applications (Wiley-Blackwell), 2023, Vol 17, Issue 12, p1732
- ISSN
1751-8644
- Publication type
Article
- DOI
10.1049/cth2.12507