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 …

Spatial–spectral classification (SSC) has become a trend for hyperspectral image (HSI)
classification. However, most SSC methods mainly consider local information, so that some …

In this paper, an efficient new hybrid time series forecasting model combining variational
mode decomposition (VMD) and Deep learning mixed Kernel ELM (MKELM) Autoencoder …

Abstract The Extreme Learning Machine is a single-hidden-layer feedforward learning
algorithm, which has been successfully applied in regression and classification problems in …

In the past decade, deep learning techniques have powered many aspects of our daily life,
and drawn ever-increasing research interests. However, conventional deep learning …

This letter identifies original independent works in the domain of randomization-based
feedforward neural networks. In the most common approach, only the output layer weights …

This article is focused on the realtime monitoring and control aspects of ICPSs. Advanced
approaches and potential challenges are illustrated in the following sections. Especially, an …

This paper presents a novel integrated approach to deal with the decision making and
motion planning for lane-change maneuvers of autonomous vehicle (AV) considering social …

Multiple kernel clustering (MKC) optimally utilizes a group of pre-specified base kernels to
improve clustering performance. Among existing MKC algorithms, the recently proposed late …

This paper proposes a data-driven approach for optimal power flow (OPF) based on the
stacked extreme learning machine (SELM) framework. SELM has a fast training speed and …