Spline adaptive filtering algorithm based on Heaviside step function-Reference-Cited by-同舟云学术

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Spline adaptive filtering algorithm based on Heaviside step function

Published:2022-01-17 Issue:5 Volume:16 Page:1333-1343
ISSN:1863-1703
Container-title:Signal, Image and Video Processing
language:en
Short-container-title:SIViP

Author:

Guan Sihai,Cheng Qing,Zhao Yong,Biswal Bharat

Funder

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Natural Science Foundation of Henan Polytechnic University

the Wuhu and Xidian University special fund for industry-university-research cooperation

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Signal Processing

Link

https://link.springer.com/content/pdf/10.1007/s11760-021-02085-z.pdf

Reference47 articles.

1. Scarpiniti, M., Comminiello, D., Scarano, G., Parisi, R., Uncini, A.: Steady-state performance of spline adaptive filters. IEEE Trans. Signal Process. 64(4), 816–828 (2016). https://doi.org/10.1109/TSP.2015.2493986

2. Scarpiniti, M., Comminiello, D., Parisi, R., Uncini, A.: Nonlinear system identification using IIR spline adaptive filters. Signal Process. 108, 30–35 (2015). https://doi.org/10.1016/j.sigpro.2014.08.045

3. Wang, W., Zhao, H., Zeng, X., Dogancay, K.: Steady-state performance analysis of nonlinear spline adaptive filter under maximum correntropy criterion. IEEE Trans. Circuits Syst. II Express Briefs 67(6), 1154–1158 (2020). https://doi.org/10.1109/TCSII.2019.2929536

4. Yang, L., Liu, J., Yan, R., Chen, X.: Spline adaptive filter with arctangent-momentum strategy for nonlinear system identification. Signal Process. 164, 99–109 (2019). https://doi.org/10.1016/j.sigpro.2019.06.007

5. Yu, T., Li, W., Yu, Y., de Lamare, R.C.: Robust spline adaptive filtering based on accelerated gradient learning: design and performance analysis. Signal Process. (2021). https://doi.org/10.1016/j.sigpro.2021.107965

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