In this paper, the noise-suppressing zeroing neural network models (NSZNNMs) for online
solving time-varying matrix square roots problems (TVMSRPs) are revisited and redesigned …

Linear equations (LEs) play an essential role in mathematics. Nevertheless, the vast majority
of fixed-parameter zeroing neural network (ZNN) models are not fast enough to solve LEs …

Neural-dynamics methods for solving quadratic programming (QP) have been studied for
decades. The main feature of a neural-dynamics solver is that it can generate a continuous …

Zeroing neural network (ZNN, or termed Zhang neural network after its inventors) is an
effective approach to dynamic matrix square root (DMSR) finding arising in numerous fields …

In this paper, a new design formula is presented to accelerate the convergence speed of a
recurrent neural network, and applied to time-varying matrix square root finding in real time …

Zeroing neural networks (ZNN) have shown their state-of-the-art performance on dynamic
problems. However, ZNNs are vulnerable to perturbations, which causes reliability concerns …

This paper presents designs of fixed-time zeroing neural networks (FxZNNs) for solving time-
variant convex optimization problems, especially arising from repetitive motion planning of …

In this paper, a finite-time convergent Zhang neural network (ZNN) is proposed and studied
for matrix square root finding. Compared to the original ZNN (OZNN) model, the finite-time …

The Zhang neural network (ZNN) has attracted a great deal of interest from a large number
of researchers because of its significant advantage in solving the various time-varying …

Nonlinear phenomena are often encountered in various practical systems, and most of the
nonlinear problems in science and engineering can be simply described by nonlinear …