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 …

Following the idea of using first-order time derivatives, this paper presents a general
recurrent neural network (RNN) model for online inversion of time-varying matrices. Different …

Background: It is important to be able to predict, for each individual patient, the likelihood of
later metastatic occurrence, because the prediction can guide treatment plans tailored to a …

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 …

In the previous work, Zhang et al. developed a special type of recurrent neural networks
called Zhang neural network (ZNN) with continuous-time and discrete-time forms for time …

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 …

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 …

Thin film transistor liquid crystal displays (TFT-LCDs) have become increasingly popular and
dominant as display devices. Surface defects on TFT panels not only cause visual failure …

In power engineering, analysis of the thermal behavior of steam turbine and development of
control strategies depend on online evaluations of the temperature fields of in-service …

In this paper, we present, develop and investigate a special kind of recurrent neural network
termed Zhang neural network (ZNN) for time-varying matrix inversion. Comparing with the …