Fast and accurate fault diagnosis is important to enhance the reliability of power converters.
Since both open-circuit faults in insulated gate bipolar translator (IGBTs) and diodes …

This paper introduces the kernel least lncosh (KLL) algorithm, in which the lncosh (logarithm
of hyperbolic cosine) cost function is successfully applied in the reproducing-kernel-Hilbert …

This paper develops adaptive graph filters that operate in reproducing kernel Hilbert spaces.
We consider both centralized and fully distributed implementations. We first define nonlinear …

Kernel adaptive filters (KAFs) generate a linear growing radial basis function (RBF) network
with the number of training samples, thereby lacking sparseness. To deal with this …

A kernel affine projection-like algorithm (KAPLA) is proposed in reproducing kernel Hilbert
space in non-Gaussian environments. The cost function for the developed algorithm is …

In this brief, a parallel kernel data-reusing maximum correntropy (PKDRMC) algorithm is
proposed within the framework of nonlinear adaptive filtering. The PKDRMC algorithm …

Nowadays, in the field of nonlinear system identification, the function approximation builds
on the theory of reproducing kernel Hilbert spaces (RKHS) is of high importance in kernel …

In this paper, a kernel recursive maximum Versoria-like criterion (KRMVLC) algorithm has
been constructed, derived, and analyzed within the framework of nonlinear adaptive filtering …

Nowadays, the kernel methods are increasingly developed, they are a significant source of
advances, not only in terms of computational cost but also in terms of the obtained …

The errors-in-variables (EIV) system model has been widely studied under the assumption
that both input and output signals are contaminated with noise. For the in-network distributed …