Part I of this paper reviewed the development of the linear active noise control (ANC)
technique in the past decade. However, ANC systems might have to deal with some …

Soft-sensors usually assist in dealing with the unavailability of hardware sensors in process
industries, thus allowing for less fault occurrence and better control performance. However …

This paper presents an implementation of a three phase distribution static compensator
(DSTATCOM) using a back propagation (BP) control algorithm for its functions such as …

In this letter, a robust kernel adaptive algorithm, called the kernel recursive maximum
correntropy (KRMC), is derived in kernel space and under the maximum correntropy …

In a recent paper, we developed a novel quantized kernel least mean square algorithm, in
which the input space is quantized (partitioned into smaller regions) and the network size is …

Gaussian processes (GPs) are versatile tools that have been successfully employed to solve
nonlinear estimation problems in machine learning but are rarely used in signal processing …

This brief presents a framework of retargeted least squares regression (ReLSR) for
multicategory classification. The core idea is to directly learn the regression targets from data …

A realistic and comprehensive review of joint approaches to machine learning and signal
processing algorithms, with application to communications, multimedia, and biomedical …

Kernel recursive least squares (KRLS) is a kind of kernel methods, which has attracted wide
attention in the research of time series online prediction. It has low computational complexity …

Anomaly detection plays an important role in industry, especially in ensuring system safety
and product quality. Due to the unavailability of anomalous data in many practical cases …