As a common mathematical operation, the time-varying matrix inversion (TVMI) is frequently
arisen in many complex problems. It has been proved in a large number of studies that the …

In this paper, a new interference-tolerant fast convergence zeroing neural network
(ITFCZNN) using a novel activation function (NAF) for solving dynamic matrix inversion …

We are concerned with the solution of the matrix equation AXB= D in real time by means of
the gradient based neural network (GNN) model, called GNN (A, B, D). The convergence …

The main propose of this paper is to develop two neural network models for solving
nonsingular multi-linear tensor system. Theoretical analysis shows that each of the neural …

An integration-enhanced noise-tolerant zeroing neural network (IENTZNN) model for
computing various expressions involving outer inverses is defined and considered. The …

An improved activation function, termed extended sign-bi-power (Esbp), is proposed. An
extension of the Li zeroing neural network (ELi-ZNN) based on the Esbp activation is …

In this paper, a noise-tolerant fast convergence zeroing neural network (NTFCZNN)
adopting a new power-versatile activation function (PVAF) is proposed and analyzed for …

New dynamical models for solving the matrix equations BX= D and XC= D are developed in
time-invariant case. These models are derived as a combination of GNN and ZNN models …

By exploiting two simplified nonlinear activation functions, two zeroing neural network (ZNN)
models are designed and studied to efficiently tackle the time-varying matrix …

Nonlinear gradient dynamic approach for solving the tensor complementarity problem (TCP)
are presented. Theoretical analysis shows that each of the defined dynamical system …