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.

中文翻译：

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具有可变风险敏感参数和内核宽度的鲁棒自适应滤波

相似性度量在自适应滤波中起着重要作用。先前的工作，如相关熵和核风险敏感损失（KRSL），在对异常值的鲁棒性、快速收敛速度和高过滤精度方面成功地改进了自适应滤波技术。在 KRSL 的基础上，通过将 KRSL 扩展到复数域，提出了一种新提出的相似性度量，即复数 KRSL（CKRSL）。在自适应滤波算法中，它成功地获得了优于其他相似性度量的性能。但是，当参数选择不当时，最小 CKRSL (MCKRSL) 算法可能会导致性能不佳。在这封信中，提出了一种自适应参数选择，以帮助 MCKRSL 算法提高性能，同时克服人工选择中的不确定性。提出的具有可变参数的 MCKRSL (MCKRSL-VP) 算法通过使迭代平方偏差尽可能小来更新风险敏感参数和内核宽度。进一步使用移动平均方案来平滑更新风险敏感参数和内核宽度。最后，作者通过仿真验证了 MCKRSL-VP 的性能优于其他算法。