Unbiased identification of a class of multi-input single-output systems with correlated disturbances using bias compensation methods
Unbiased identification of a class of multi-input single-output systems with correlated disturbances using bias compensation methods
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DOI：
10.1016/j.mcm.2010.12.059
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发表时间：
2011-05
期刊：
影响因子：
--
通讯作者：
Yong Zhang
中科院分区：
文献类型：
--
作者：
Yong Zhang
本文研究了带有色噪声的多输入单输出（MISO）系统的建模与辨识问题。为了得到系统的无偏递推估计，本文提出了一种基于偏差补偿技术的递推最小二乘（RLS）辨识算法。其基本思想是通过在最小二乘（LS）估计中加入校正项来消除估计偏差，设计一组稳定的数字预滤波器对多输入通道的输入采样数据进行预处理，以消除LS估计中有色噪声引起的偏差项，进而推导出基于偏差补偿的RLS算法。所开发的方法的性能进行了理论分析，并通过仿真结果显示。
This paper studies the modelling and identification problems for multi-input single-output (MISO) systems with colored noises. In order to obtain the unbiased recursive estimates of the systems, this paper presents a recursive least squares (RLS) identification algorithm based on bias compensation technique. The basic idea is to eliminate the estimation bias by adding a correction term in the least squares (LS) estimates, a set of stable digital prefilters are suitably designed to preprocess the input sampled data from multi-input channels for the purpose of getting the bias term arisen by colored noises in LS estimates, and further to derive a bias compensation based RLS algorithm. The performance of the developed method is both analyzed theoretically and shown by means of simulation results.