A RNN for Solving Discrete-Form Time-Varying Matrix Inversion: From Model Design to Parameter Analysis

In this paper, the 5-instant discretization formula (5IDF), renowned for its high computational accuracy in approximating the first-order derivative, is employed for the discretization of the continuous-time zeroing neural network (CTZNN). The formula is then compared with other common difference formulas of varying precision. Based on this formulation, the 5IDF-type discrete-time zeroing neural network (DTZNN) model is introduced and studied for solving discrete-form time-varying matrix inversion. The feasibility, effectiveness, and superiority of the proposed 5IDF-type DTZNN model for addressing discrete-form time-varying matrix inversion are further substantiated through numerical experiments.