The zeroing neural network (ZNN) model, a powerful approach for addressing time-varying
problems, has been extensively applied in the calculation and optimization fields. In this …

This article proposes an improved general zeroing neural network model to suppress noise
and to enhance the real-time performance of solving TVQP problems. The proposed model …

While the recurrent neural network (RNN) has achieved remarkable performance on
dynamic and control tasks, its applications to image processing, particularly target detection …

This study proposes a novel gradient‐based neural network model with an activated
variable parameter, named as the activated variable parameter gradient‐based neural …

This paper proposes a new zeroing neural network (NZNN) for solving the dynamic complex
value linear equation (DCVLE). To achieve a faster convergence rate and improve the …

Zeroing neural network (ZNN) model is a powerful tool for solving time-varying nonlinear
minimization problems. This study presents some limitations of existing ZNN models, mainly …

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 …

Motivated from the convergence capability achieved by gradient neural network (GNN) and
zeroing neural network (ZNN) for matrix inversion, in this article, a novel hybrid GNN-ZNN (H …

The online solving of a dynamic linear complex matrix equation (DLCME) is commonly
encountered in many fields, and it exists for lots of engineering applications. For solving the …

Neural dynamics remains a crucial field of interest for researchers, owing to its extensive
applicability in addressing time-varying challenges across diverse domains. This study …