As a robust nonlinear similarity measure in kernel space, correntropy has received
increasing attention in domains of machine learning and signal processing. In particular, the …

Nonlinear similarity measures defined in kernel space, such as correntropy, can extract
higher order statistics of data and offer potentially significant performance improvement over …

In recent years, more and more wearable sensors have been employed in smart health
applications. Wearable sensors not only can be used to collect valuable health-related data …

Disturbance observers have been attracting continuing research efforts and are widely used
in many applications. Among them, the Kalman filter-based disturbance observer is an …

State estimation (SE) plays a pivotal role in the development of modern power system.
Accurate forecasting-aided state estimation (FASE) can track the sudden changes of power …

The traditional unscented Kalman filter (UKF) with mean square error (MSE) criterion for
dynamic state estimation (DSE) is sensitive for unknown non-Gaussian noise and outliers …

As a variant of the support vector machine (SVM), the twin support vector machine (TSVM)
has attracted substantial attention; however, TSVM is sensitive to outliers. To remedy it, this …

The correntropy-induced loss (C-loss) function has the nice property of being robust to
outliers. In this paper, we study the C-loss kernel classifier with the Tikhonov regularization …

In the Internet of Things (IoT), asynchronous networks with varying topology are quite
common. Meanwhile, Gaussian noise and impulsive noise widely exist in asynchronous …

More recently, extreme learning machine (ELM) has emerged as a novel computing
paradigm that enables the neural network (NN) based learning to be achieved with fast …