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Gradient-based recursive parameter estimation for a periodically nonuniformly sampled-data Hammerstein–Wiener system based on the key-term separation
International Journal of Adaptive Control and Signal Processing ( IF 3.1 ) Pub Date : 2021-07-01 , DOI: 10.1002/acs.3296 Qilin Liu 1 , Feng Ding 1
International Journal of Adaptive Control and Signal Processing ( IF 3.1 ) Pub Date : 2021-07-01 , DOI: 10.1002/acs.3296 Qilin Liu 1 , Feng Ding 1
Affiliation
The identification of the Hammerstein–Wiener (H-W) systems based on the nonuniform input–output dataset remains a challenging problem. This article studies the identification problem of a periodically nonuniformly sampled-data H-W system. In addition, the product terms of the parameters in the H-W system are inevitable. In order to solve the problem, the key-term separation is applied and two algorithms are proposed. One is the key-term-based forgetting factor stochastic gradient (KT-FFSG) algorithm based on the gradient search. The other is the key-term-based hierarchical forgetting factor stochastic gradient (KT-HFFSG) algorithm. Compared with the KT-FFSG algorithm, the KT-HFFSG algorithm gives more accurate estimates. The simulation results indicate that the proposed algorithms are effective.
中文翻译:
基于关键项分离的周期性非均匀采样 Hammerstein-Wiener 系统的基于梯度的递归参数估计
基于非均匀输入输出数据集的 Hammerstein-Wiener (HW) 系统的识别仍然是一个具有挑战性的问题。本文研究了周期性非均匀采样数据硬件系统的识别问题。此外,硬件系统中参数的乘积项是不可避免的。为了解决这个问题,应用了关键项分离,并提出了两种算法。一种是基于梯度搜索的基于关键词的遗忘因子随机梯度(KT-FFSG)算法。另一种是基于关键词的分层遗忘因子随机梯度(KT-HFFSG)算法。与 KT-FFSG 算法相比,KT-HFFSG 算法给出了更准确的估计。仿真结果表明所提出的算法是有效的。
更新日期:2021-07-01
中文翻译:
基于关键项分离的周期性非均匀采样 Hammerstein-Wiener 系统的基于梯度的递归参数估计
基于非均匀输入输出数据集的 Hammerstein-Wiener (HW) 系统的识别仍然是一个具有挑战性的问题。本文研究了周期性非均匀采样数据硬件系统的识别问题。此外,硬件系统中参数的乘积项是不可避免的。为了解决这个问题,应用了关键项分离,并提出了两种算法。一种是基于梯度搜索的基于关键词的遗忘因子随机梯度(KT-FFSG)算法。另一种是基于关键词的分层遗忘因子随机梯度(KT-HFFSG)算法。与 KT-FFSG 算法相比,KT-HFFSG 算法给出了更准确的估计。仿真结果表明所提出的算法是有效的。