Research Article
Multi-convex Combination Adaptive Filtering Algorithm Based on Maximum Versoria Criterion (Workshop)
@INPROCEEDINGS{10.1007/978-3-030-41117-6_24, author={Wenjing Wu and Zhonghua Liang and Yimeng Bai and Wei Li}, title={Multi-convex Combination Adaptive Filtering Algorithm Based on Maximum Versoria Criterion (Workshop)}, proceedings={Communications and Networking. 14th EAI International Conference, ChinaCom 2019, Shanghai, China, November 29 -- December 1, 2019, Proceedings, Part II}, proceedings_a={CHINACOM PART 2}, year={2020}, month={2}, keywords={Maximum Versoria Criterion (MVC) Multi-convex combination Multi-convex combination maximum correntropy criterion (MCMCC) Steady-state error}, doi={10.1007/978-3-030-41117-6_24} }- Wenjing Wu
Zhonghua Liang
Yimeng Bai
Wei Li
Year: 2020
Multi-convex Combination Adaptive Filtering Algorithm Based on Maximum Versoria Criterion (Workshop)
CHINACOM PART 2
Springer
DOI: 10.1007/978-3-030-41117-6_24
Abstract
Aiming at the contradiction between the convergence rate and steady state mean square error of adaptive filter based on Maximum Versoria Criterion (MVC), this paper introduces the multi-convex combination strategy into MVC algorithm, and proposes a multi-convex combination MVC (MCMVC) algorithm. Simulation results show that compared with the existing MVC algorithm, MCMVC algorithm can select the best filter more flexibly under different weight change rates, and thus it has faster convergence speed and stronger tracking ability. Moreover, compared with the existing multi-convex combination maximum correntropy criterion (MCMCC) algorithm, MCMVC algorithm not only ensures the tracking performance, but also has lower exponential computation and steady-state error.
