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

作　　者：JING Lida ZHANG Ji-Feng 

机构地区：[1]Key Laboratory of Systems and Control,Institute of Systems Science,Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing 100190,China [2]School of Mathematical Sciences,University of Chinese Academy of Sciences,Beijing 100049,China

出　　处：《Journal of Systems Science & Complexity》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。

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.

关 键 词：Discrete-time linear time-invariant systems parameter estimation quantized output 

分 类 号：O212.1[理学—概率论与数理统计]