A noise-tolerant fast convergence ZNN for dynamic matrix inversion
Jie Jin, Jianqiang Gong
Abstract
In this paper, a noise-tolerant fast convergence zeroing neural network (NTFCZNN) adopting a new power-versatile activation function (PVAF) is proposed and analyzed for solving dynamic matrix inversion (DMI). The proposed NTFCZNN has not only the fixed-time convergence ability but also the strong noise-tolerant ability when it is used to solve DMI problems. The new NTFCZNN and the original ZNN (OZNN) activated by the recently reported universal AF (versatile AF) are simultaneously used in the matrix inverse problem under the context of all kinds of distractions. Then, through a comprehensive comparative analysis of the simulation results, the powerful anti-disturbance ability of NTFCZNN is better highlighted. Both the theoretical verification under various circumstances and the sharp contrast simulation experiments are sufficient to show that the NTFCZNN model has high reliability and noise resistance in the process of solving the DMI problems.