Bartels–Stewart algorithm is an effective and widely used method with an O (n 3) time
complexity for solving a static Sylvester equation. When applied to time-varying Sylvester …

Solving Sylvester equation is a common algebraic problem in mathematics and control
theory. Different from the traditional fixed-parameter recurrent neural networks, such as …

This technical note presents theoretical analysis and simulation results on the performance
of a classic gradient neural network (GNN), which was designed originally for constant …

As a type of recurrent neural networks (RNNs) modeled as dynamic systems, the gradient
neural network (GNN) is recognized as an effective method for static matrix inversion with …

Different from gradient-based neural dynamics, a special kind of recurrent neural dynamics
has recently been proposed by Zhang et al. for solving online time-varying problems. Such a …

Different from gradient-based neural networks, a special kind of recurrent neural network
(RNN) has recently been proposed by Zhang for online matrix inversion. Such an RNN is …

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

Zhang neural networks (ZNN), a special kind of recurrent neural networks (RNN) with
implicit dynamics, have recently been introduced to generalize to the solution of online time …

We present a unit that performs continuous-time hybrid approximate computation, in which
both analog and digital signals are functions of continuous time. Our 65 nm CMOS prototype …

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 …