论文标题
自适应随机傅立叶功能内核LMS
Adaptive Random Fourier Features Kernel LMS
论文作者
论文摘要
我们提出了自适应随机傅立叶特征高斯内核LMS（ARFF-GKLMS）。像大多数基于随机梯度下降的内核自适应过滤器一样，该算法使用预设数量的随机傅立叶功能来节省计算成本。但是，作为一个额外的灵活性，它可以以在线方式以随机傅立叶功能来调整固有的内核带宽。这种适应机制可以减轻事先选择内核带宽的问题，以便在非平稳情况下进行改进的跟踪。仿真结果证实，所提出的算法在具有预设内核带宽的其他内核自适应过滤器上，在收敛率，稳态误差以及跟踪能力方面取得了改善。
We propose the adaptive random Fourier features Gaussian kernel LMS (ARFF-GKLMS). Like most kernel adaptive filters based on stochastic gradient descent, this algorithm uses a preset number of random Fourier features to save computation cost. However, as an extra flexibility, it can adapt the inherent kernel bandwidth in the random Fourier features in an online manner. This adaptation mechanism allows to alleviate the problem of selecting the kernel bandwidth beforehand for the benefit of an improved tracking in non-stationary circumstances. Simulation results confirm that the proposed algorithm achieves a performance improvement in terms of convergence rate, error at steady-state and tracking ability over other kernel adaptive filters with preset kernel bandwidth.
