Extreme learning machine (ELM) has gained increasing interest from various research fields
recently. In this review, we aim to report the current state of the theoretical research and …

The identification of non-linear systems using only observed finite datasets has become a
mature research area over the last two decades. A class of linear-in-the-parameter models …

Data-driven discovery of dynamics via machine learning is pushing the frontiers of modelling
and control efforts, providing a tremendous opportunity to extend the reach of model …

Online battery capacity estimation is a critical task for battery management system to
maintain the battery performance and cycling life in electric vehicles and grid energy storage …

Extreme learning machines (ELMs) have proven to be efficient and effective learning
mechanisms for pattern classification and regression. However, ELMs are primarily applied …

Accurate estimations of battery state-of-charge (SOC) for energy storage systems are
popular research topics in recent years. Numerous challenges remain in several aspects …

Accurate pattern transfer, coupled with stringent requirements for throughput and quality in
lithography systems, necessitates the wafer scanner to execute an aggressive motion with …

Extreme learning machine (ELM), as a new learning framework, draws increasing attractions
in the areas of large-scale computing, high-speed signal processing, artificial intelligence …

Feedforward neural networks (FFNN) have been utilised for various research in machine
learning and they have gained a significantly wide acceptance. However, it was recently …

Battery temperature is a primary factor affecting the battery performance, and suitable battery
temperature control in particular internal temperature control can not only guarantee battery …