Robust adaptive estimator based on a novel objective function—Using the L1-norm and L0-norm

作     者：Sihai Guan Chuanwu Zhang Guofu Wang Bharat Biswal 

作者机构：Key Laboratory of Electronic and Information EngineeringState Ethnic Affairs CommissionChengdu610041China College of Electronic and InformationSouthwest Minzu UniversityChengdu610041China Center for Materials Science and EngineeringSchool of Electrical and Information EngineeringGuangxi University of Science and TechnologyLiuzhou545006China Department of Biomedical EngineeringNew Jersey Institute of TechnologyNewarkNJ07102USA The Clinical Hospital of Chengdu Brain Science InstituteMOE Key Laboratory for NeuroinformationCenter for Information in MedicineSchool of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengdu 611731China 

出 版 物：《Journal of Automation and Intelligence》 (自动化与人工智能（英文）)

年 卷 期：2023年第2卷第2期

页      面：105-117页

学科分类：07[理学] 070104[理学-应用数学] 0701[理学-数学] 

基　　金：supported by the National Key Research and Development Program of China(2022YFE0134600) the National Natural Science Foundation of China(61871420) the Sichuan Science and Technology Program,China(23NSFSC2916) the introduction of Talent,Southwest MinZu University,China,funding research projects start(RQD2021064) 

主　　题：Adaptive filter LMS LMAT SELMS Multiple types of noises 

摘      要：To fully take advantage of LMS,LMAT,and SELMS,a novel adaptive estimator using the L1-norm and L0-norm of the estimated error is proposed in this *** based on minimizing the mean-square deviation at the current time,the optimal step-size,parameters𝛿and𝜃of the proposed adaptive estimator are ***,the stability and computational complexity of the mean estimation error is analyzed *** results(both simulation and real mechanical system datasets)show that the proposed adaptive estimator is more robust to input signals and a variety of measurement noises(Gaussian and non-Gaussian noises).In addition,it is superior to LMS,LMAT,SELMS,the convex combination of LMS and LMAT algorithm,the convex combination of LMS and SELMS algorithm,and the convex combination of SELMS and LMAT *** theoretical analysis is consistent with the Monte-Carlo *** of them show that the adaptive estimator has an excellent performance in the estimation of unknown linear systems under various measurement noises.