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 …

Due to the rapid development of deep learning techniques, numerous frameworks including
convolutional neural networks (CNNs), deep belief networks (DBNs) and auto-encoder (AE) …

In this paper, we present adaptive neural network tracking control of a robotic manipulator
with input deadzone and output constraint. A barrier Lyapunov function is employed to deal …

Most recently, deep learning-based visual detection has attracted rapidly increasing
attention paid to marine organisms, thereby expecting to significantly benefit ocean ecology …

In this paper, a novel adaptive robust online constructive fuzzy control (AR-OCFC) scheme,
employing an online constructive fuzzy approximator (OCFA), to deal with tracking surface …

Conventional extreme learning machines (ELMs) solve a Moore–Penrose generalized
inverse of hidden layer activated matrix and analytically determine the output weights to …

In this paper, a finite-time observer based accurate tracking control (FO-ATC) scheme is
addressed for trajectory tracking of a marine vehicle (MV) with complex unknowns including …

In this paper, model identification and adaptive control design are performed on Devanit-
Hartenberg model of a humanoid robot. We focus on the modeling of the 6 degree-of …

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 …

In this paper, a fixed-time control method is proposed for an uncertain robotic system with
actuator saturation and constraints that occur a period of time after the system operation. A …