论文丨Xue-Tao XIE, Zhi-Ping LI, Yi-Fei PU*, Jian WANG, Wei-Hua ZHANG, Yang WEN：A Fractional Filter Based on Reinforcement Learning for Effective Tracking Under Impulsive Noise

本文（A Fractional Filter Based on Reinforcement Learning for Effective Tracking Under Impulsive Noise）原载Neurocomputing，由四川大学蒲亦非教授等科研人员创作，系川大学智慧法治超前部署学科系列学术成果。后续会持续分享四川大学智慧法治超前部署学科系列学术成果，欢迎大家阅读。

Abstract: It is valuable and meaningful to suppress impulsive noise in system identification. Existing algorithms usually only consider impulsive noise with small frequency and amplitude. Furthermore, few researchers pay attention to the tracking performance of these algorithms. This paper builds a framework based on the deep deterministic policy gradient (DDPG) algorithm with the ability to explore and correct. The enhanced fractional derivative is introduced to further improve the performance of this reinforcement learning-based framework. Thus a fractional least mean square filter algorithm based on reinforcement learning (FrlMS) is proposed. The stability of the FrlMS algorithm is analyzed. Compared with the competing algorithms, the simulation experiments in system identification show that the FrlMS algorithm has satisfactory tracking performance in the face of impulsive noise, especially when the frequency and (or) amplitude are (is) large.

Xue-Tao XIE, Zhi-Ping LI, Yi-Fei PU*, Jian WANG, Wei-Hua ZHANG, Yang WEN. “A Fractional Filter Based on Reinforcement Learning for Effective Tracking Under Impulsive Noise,” Neurocomputing, vol. 516, pp. 155-168, 2023. （论文下载）