System identification via data of finite wordlengths
System identification via data of finite wordlengths
复制标题
通过有限字长数据进行系统识别
DOI：
10.1109/sice.2002.1196534
复制
发表时间：
2002
期刊：
影响因子：
--
通讯作者：
R. Takahashi
中科院分区：
文献类型：
--
作者：
A. Okao;M. Ikeda;R. Takahashi
对于连续时间对象的系统辨识，我们通常使用从模拟信号转换的数字数据。由于数字变量是有限字长的，它们包含量化误差。这样的误差可能显著地降低识别精度，特别是在需要大的移动和精确定位的机械系统的情况下。为了克服这个问题，本文提出了估计量化误差，从而真正的模拟采样信号，以提高识别精度。量化误差的估计和系统辨识同时进行。
For system identification of continuous-time plants, we commonly use digital data converted from analog signals. Since digital variables are of finite wordlengths, they contain quantization errors. Such errors may deteriorate identification accuracy significantly, especially in the case of mechanical systems where large movement and precise positioning are required. To overcome this problem, the present paper proposes to estimate the quantization errors and thus true analog sampled signals to improve identification accuracy. Estimation of quantization errors and system identification are carried out simultaneously.