Matlab Simulink of Varying-Parameter Convergent-Differential Neural-Network for Solving Online Time-Varying Matrix Inverse

摘要

To deal with time-varying equations, a novel recurrent neural network, named varying-parameter convergent-differential neural network (in short, VP-CDNN), is proposed, modeled and analyzed. It is designed by a matrix-valued error function and the design parameter is time-varying, which enables the VP-CDNN to have good convergence and robustness. For illustration and comparison, a scalar-valued error function-based recurrent neural network, i.e., gradient-based neural network (GNN), is presented and developed. Matlab Simulink of both the VP-CDNN and GNN for solving online time-varying matrix inverse is constructed and presented. Comparison results verify that the proposed VP-CDNN is more effective and robust than those of GNN.