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LS-Based Parameter Estimation of DARMA Systems with Uniformly Quantized Observations

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摘要 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.
出处 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2022年第3期748-765,共18页 系统科学与复杂性学报(英文版)
基金 supported by National Key R&D Program of China under Grant No.2018YFA0703800 the National Natural Science Foundation of China under Grant No.61877057。
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