发表机构
Southwest Jiaotong University（西南交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有非线性自适应滤波算法忽视输入噪声及BCKLMS算法局限的问题，提出RFFBCGA算法，在RFFBC框架内，通过BC项减轻输入噪声干扰、改善信号表征，利用GA函数增强鲁棒性，仿真验证了该方法的优越性。
AI 中文摘要
大多数现有非线性自适应滤波算法只考虑输出噪声，忽视输入噪声在实际中也普遍存在这一事实。虽最近提出的偏差补偿核最小均方（BCKLMS）算法解决了非线性变量误差（EIV）模型中的输入噪声问题，但仍有两个主要局限。本文提出随机傅里叶偏差补偿通用自适应函数滤波器（RFFBCGA）算法。在基于随机傅里叶特征的偏差补偿（RFFBC）框架内，该算法不仅保持固定网络结构并通过BC项有效减轻输入噪声干扰，还改善了对输入信号的表征。通过利用通用自适应（GA）函数的灵活形式，进一步增强了算法在各种噪声场景下的鲁棒性。大量仿真，包括实际时间序列预测任务，证明了该方法的优越性。
英文摘要
Most existing nonlinear adaptive filtering algorithms only account for output noise, neglecting the fact that input noise is also prevalent in practice. Although the recently proposed bias-compensated kernel least mean square (BCKLMS) algorithm addresses input noise in the nonlinear errors-in-variables (EIV) model, it still suffers from two major limitations. First, the use of a fixed-size dictionary restricts network growth but also prevents it from fully capturing the characteristics of the input signal. Second, as an least mean square (LMS) based algorithm, it exhibits poor robustness in the presence of non-Gaussian noise in the output signal. To overcome these issues, this paper proposes the random Fourier bias-compensated filter under general adaptive function (RFFBCGA) algorithm. Within the random Fourier feature based bias-compensated (RFFBC) framework, the proposed algorithm not only maintains a fixed network structure and effectively mitigates input noise interference through the BC term, but also achieves improved characterization of the input signal. Moreover, by leveraging the flexible form of the general adaptive (GA) function, the algorithm's robustness across various noise scenarios is further enhanced. Extensive simulations, including real-world time series prediction tasks, demonstrate the superiority of the proposed method.
