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

Document Type

Article

Publication Date

2-1-2023

Abstract

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 SAF-RMSProp 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.

Identifier

85179513409 (Scopus)

Publication Title

Journal of Automation and Intelligence

External Full Text Location

https://doi.org/10.1016/j.jai.2022.100008

e-ISSN

29498554

First Page

1

Last Page

13

Issue

1

Volume

2

Grant

61871420

Fund Ref

National Natural Science Foundation of China

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