A novel recursive learning estimation algorithm of Wiener systems with quantized observations. (June 2021) - Li, Linwei; Wang, Fengxian; Zhang, Huanlong; Ren, Xuemei
Abstract: 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. Highlights: The filter can adjust the filtering performance in real time using the system data. The modified gain can increase the modified performance based the data vector. The loss function has a better nature including estimated error and initial data.