Kriging models, also known as Gaussian process models, are widely used in surrogate-
assisted evolutionary algorithms (SAEAs). However, the cubic time complexity of the …

Battery state foretasting and health management are significant tasks for ensuring safety and
stability of battery systems. Accurate state estimation can not only provide valuable …

Sequential Bayesian inference over predictive functions is a natural framework for continual
learning from streams of data. However, applying it to neural networks has proved …

The variation tendency of oxygen demand plays a crucial role in planning and scheduling in
the steel industry. Traditional prediction methods merely utilize historical data without …

Simultaneous prediction of wrist and hand motions is essential for the natural interaction
with hand prostheses. In this paper, we propose a novel multi-out Gaussian process (MOGP) …

This article proposes a novel adaptive robust control approach based on Gaussian
processes (GPs) for the high-precision tracking problem of uncertain Euler–Lagrange (EL) …

Accurate sensing is essential for achieving reliable control of unmanned aerial vehicles
(UAVs). In prior works, the unscented Kalman filter (UKF) has shown superior performance …

In this paper we consider a new fault detection approach that merges the benefits of
Gaussian process regression (GPR) with a generalized likelihood ratio test (GLRT). The …

In this paper, the precise control of the underwater manipulator has studied under the
conditions of uncertain underwater dynamics and time-varying external interference. An …

Gaussian process regression (GPR) has shown superiority in terms of state estimation for its
nonparametric characteristic and uncertainty prediction ability. Due to its heavy …