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.

本文涉及具有量化数据的确定性自回归移动平均 (DARMA) 系统的参数估计。这里采用的估计算法是最小二乘法（LS）和遗忘因子LS，信号量化器是统一的，即具有统一的量化误差。作者分析了 LS 和遗忘因子 LS 的性质，建立了估计误差的有界性以及估计误差与量化误差大小的关系，即量化误差越小，估计误差越小。错误是。给出了一个数值例子来证明定理。