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Hierarchical recursive least squares algorithms for Hammerstein nonlinear autoregressive output-error systems
International Journal of Adaptive Control and Signal Processing ( IF 3.1 ) Pub Date : 2021-08-19 , DOI: 10.1002/acs.3320
Zhen Kang 1 , Yan Ji 1 , Ximei Liu 1
Affiliation  

This article considers the parameter estimation problem of Hammerstein nonlinear autoregressive output-error systems with autoregressive moving average noises. Applying the key term separation technique, the original system is decomposed into three subsystems: the first subsystem contains the unknown parameters related to the output, the second subsystem contains the unknown parameters related to the input, and the third subsystem contains the unknown parameters related to the noise model. A hierarchical recursive least squares algorithm is proposed based on the hierarchical identification principle for interactively identifying each subsystem. The simulation results confirm that the proposed algorithm is effective in estimating the parameters of Hammerstein nonlinear autoregressive output-error systems.

中文翻译:

Hammerstein非线性自回归输出误差系统的分层递归最小二乘算法

本文考虑具有自回归移动平均噪声的 Hammerstein 非线性自回归输出误差系统的参数估计问题。应用关键项分离技术,将原系统分解为三个子系统:第一个子系统包含与输出相关的未知参数,第二个子系统包含与输入相关的未知参数,第三个子系统包含与输入相关的未知参数。噪声模型。提出了一种基于层次识别原理的层次递推最小二乘算法,用于交互识别各个子系统。仿真结果证实了该算法在估计Hammerstein非线性自回归输出误差系统的参数方面是有效的。
更新日期:2021-08-19
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