In recent years, neural networks have become a common practice in academia for handling
complex problems. Numerous studies have indicated that complex problems can generally …

A generalized Sylvester equation is a special formulation containing the Sylvester equation,
the Lyapunov equation and the Stein equation, which is often encountered in various fields …

Aiming at time-variant problems solving, a special type of recurrent neural networks, termed
zeroing neural network (ZNN), has been proposed, developed, and validated since 2001 …

In this article, a noise-suppression variable-parameter zeroing neural network (NSVPZNN)
is proposed to handle the dynamic Sylvester equation. Differing from the previous zeroing …

This article is primarily concerned with finite-time convergence (FTC) and predefined-time
convergence (PTC) design for a class of general zeroing neural network (ZNN) by …

The Zhang neural network (ZNN) has recently realized remarkable success in solving time-
varying problems. Harmonic noise widely exists in industrial applications and can severely …

This article aims to solve the Moore-Penrose inverse of time-varying full-rank matrices in the
presence of various noises in real time. For this purpose, two varying-parameter zeroing …

This paper presents an accelerated gradient-based neural network (GNN) to achieve visual
servoing of a surgical endoscope robot. A KUKA LWR 4+ robot with seven joints is used to …

The application of zeroing neural network (ZNN) to solve multifarious time-varying problems,
especially the dynamic matrix inversion (DMI), is widely used in recent years. As the core …

In this article, a recurrent neural network (RNN) is accelerated and applied to visual servo
control of a physically constrained robotic flexible endoscope. The robotic endoscope …