System identification via data of finite wordlengths

A. Okao, M. Ikeda, R. Takahashi
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引用次数: 4

Abstract

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.
通过有限字长数据进行系统识别
对于连续时间对象的系统识别,我们通常使用由模拟信号转换而来的数字数据。由于数字变量的字长是有限的,因此它们包含量化误差。这种误差可能会大大降低识别精度,特别是在需要大运动和精确定位的机械系统的情况下。为了克服这一问题,本文提出估计量化误差,从而估计真实模拟采样信号,以提高识别精度。量化误差估计与系统辨识同时进行。
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