Open AccessRobust adaptive filtering with variable risk‐sensitive parameter and kernel width
Author(s) -
Liu Yingzhi,
Dong Fei,
Yu Xin,
Qian Guobing,
Wang Shiyuan
Publication year - 2020
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2020.0698
Subject(s) - robustness (evolution) , outlier , kernel (algebra) , algorithm , computer science , convergence (economics) , rate of convergence , adaptive filter , similarity (geometry) , variable (mathematics) , mathematics , artificial intelligence , channel (broadcasting) , computer network , mathematical analysis , biochemistry , chemistry , combinatorics , economics , image (mathematics) , gene , economic growth
Similarity measures play a significant role in adaptive filtering. Previous work such as correntropy and kernel risk‐sensitive loss (KRSL), has successfully improved the technology of adaptive filtering in terms of robustness against outliers, fast convergence speed and high filtering accuracy. Based on KRSL, a newly raised similarity measure, complex KRSL (CKRSL), was proposed by extending KRSL to the complex domain. It successfully gains superior performance than other similarity measures in adaptive filtering algorithms. However, the minimum CKRSL (MCKRSL) algorithm may result in poor performance when the parameters are not properly chosen. In this Letter, an adaptive parameter selection is proposed to help the MCKRSL algorithm improve performance while overcoming the uncertainty in artificial selection. The proposed MCKRSL with variable parameters (MCKRSL‐VP) algorithm updates the risk‐sensitive parameter and kernel width by making the iteratively squared bias as small as possible. A moving average scheme is further used to smoothly update the risk‐sensitive parameter and kernel width. Finally, the authors verify that MCKRSL‐VP performs better than other algorithms by simulations.