Li Linwei, Wang Fengxian, Zhang Huanlong, Ren Xuemei
School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, PR China.
School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, PR China.
ISA Trans. 2021 Jun;112:23-34. doi: 10.1016/j.isatra.2020.11.032. Epub 2020 Dec 2.
In this paper, a novel recursive learning identification approach is proposed to estimate the parameters of the Wiener systems with quantized output. By using a filter with adaptive performance, the data preprocessing is achieved based on the system data. To derive the error information of parameter estimation, some filtered and intermediate variables are developed. Based on the estimation error and initial parameter data, a novel loss function is established, in which the estimation precision can be raised by force of the estimation error data and the convergence rate can be improved based on the initial parameter data. By minimizing the loss function, a novel recursive learning estimator is derived where the performance of the modified gain is improved due to the utilization of the observed data. Under the continuous excitation condition, the convergence analysis shows that the estimation error can converge to zero. Finally, illustrative examples and a real-life experiment are performed to validate the obtained results and efficiency of the proposed algorithm.
本文提出了一种新颖的递归学习辨识方法，用于估计具有量化输出的维纳系统的参数。通过使用具有自适应性能的滤波器，基于系统数据实现数据预处理。为了推导参数估计的误差信息，开发了一些滤波后的中间变量。基于估计误差和初始参数数据，建立了一种新颖的损失函数，其中通过估计误差数据可提高估计精度，基于初始参数数据可提高收敛速度。通过最小化损失函数，推导了一种新颖的递归学习估计器，由于利用了观测数据，改进增益的性能得到了提升。在持续激励条件下，收敛性分析表明估计误差可收敛到零。最后，通过示例和实际实验验证了所得结果及所提算法的有效性。
