Random Fourier adaptive filters (RFAFs) project the original data into a high-dimensional
random Fourier feature space (RFFS) such that the network structure of filters is fixed while …

With the emergence of complex correntropy, the maximum complex correntropy criterion
(MCCC) has been applied to the complex-domain adaptive filtering. The MCCC uses the …

The variable step-size normalized subband adaptive filter (NSAF) with a fixed parametric
step-size scaler (SSS) results in a tradeoff between the fast convergence rate and small …

Kernel adaptive filters (KAFs) are a class of nonlinear adaptive filters developed in the
reproducing kernel Hilbert space, and are particularly suitable for addressing signal …

Since the maximum correntropy criterion (MCC) algorithm with a constant kernel width leads
to the trade-off problem between the convergence rate and steady-state misalignment …

The kernel least mean square (KLMS) algorithm is the simplest algorithm in kernel adaptive
filters. However, the network growth of KLMS is still an issue for preventing its online …

In this brief, the steady-state tracking performance of minimum kernel risk-sensitive loss in a
non-stationary environment is analyzed. In order to model a non-stationary environment, a …

Adaptive networks solve distributed optimization problems in which all agents of the network
are interested to collaborate with their neighbors to learn a similar task. Collaboration is …

Recently, the maximum complex correntropy criterion (MCCC) algorithm has shown its
superiority for adaptive filter in complex domain. Compared with the traditional mean square …

Kernel adaptive filter armed with information theoretic learning has gained popularity in the
domain of time series online prediction. In particular, the generalized correntropy criterion …