GNN Model With Robust Finite-Time Convergence for Time-Varying Systems of Linear Equations

Dynamic neural networks are considered as an effective method in the field of scientific computing, among which gradient neural networks (GNNs) are an efficient method for solving static problems. However, when solving dynamic problems, the current GNNs are often subject to lagging errors. In this article, we develop a finite-time convergent GNN (FTCGNN) model for solving static and time-varying systems of linear equations. Different from zeroing neural networks (ZNNs) dedicated to time-varying problem solving, the FTCGNN model has finite-time convergence regardless of the existence of time-varying noises. Simulation results show that the FTCGNN model is effective, among which the comparisons with existing GNNs and ZNNs validate the advantages of the FTCGNN model.