@article{Guan2023, 
author = {Sihai Guan and Bharat Biswal},
title = {Spline adaptive filtering algorithm based on different iterative gradients: Performance analysis and comparison},
year = {2023},
journal = {Journal of Automation and Intelligence},
volume = {2},
number = {1},
pages = {1-13},
keywords = {Spline adaptive filter, Multi-types iterative gradients, Step-size, Noise types, Real datasets},
url = {https://www.sciopen.com/article/10.1016/j.jai.2022.100008},
doi = {10.1016/j.jai.2022.100008},
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.}
}