Spline adaptive filtering algorithm based on different iterative gradients:Performance analysis and comparison  

作　　者：Sihai Guan Bharat Biswal 

机构地区：[1]College of Electronic and Information,Southwest Minzu University,China [2]Key Laboratory of Electronic and Information Engineering,State Ethnic Affairs Commission,China [3]The Clinical Hospital of Chengdu Brain Science Institute,MOE Key Laboratory for Neuroinformation,Center for Information in Medicine,School of Life Science and Technology,University of Electronic Science and Technology of China,Chengdu,China [4]Department of Biomedical Engineering,New Jersey Institute of Technology(NJIT),Newark,NJ,USA

出　　处：《Journal of Automation and Intelligence》2023年第1期1-13,共13页自动化与人工智能（英文）

基　　金：supported by the National Natural Science Foundation of China(61871420);the Natural Science Foundation of Sichuan Province,China(23NSFSC2916);the introduction of talent,Southwest MinZu University,China,funding research projects start(RQD2021064).

摘　　要：Two novel spline adaptive filtering(SAF)algorithms are proposed by combining different iterative gradient methods,i.e.,Adagrad and RMSProp,named SAF-Adagrad and SAF-RMSProp,in this paper.Detailed convergence performance and computational complexity analyses are carried out also.Furthermore,compared with existing SAF algorithms,the influence of step-size and noise types on SAF algorithms are explored for nonlinear system identification under artificial datasets.Numerical results show that the SAF-Adagrad and SAFRMSProp algorithms have better convergence performance than some existing SAF algorithms(i.e.,SAF-SGD,SAF-ARC-MMSGD,and SAF-LHC-MNAG).The analysis results of various measured real datasets also verify this conclusion.Overall,the effectiveness of SAF-Adagrad and SAF-RMSProp are confirmed for the accurate identification of nonlinear systems.

关 键 词：Spline adaptive filter Multi-types iterative gradients STEP-SIZE Noise types Real datasets 

分 类 号：TN713[电子电信—电路与系统]