Comprehensive Analysis of a New Varying Parameter Zeroing Neural Network for Time Varying Matrix Inversion

Abstract

The matrix inversion problem plays a very important role in mathematics as well as practical engineering applications. In this paper, unlike the traditional fixed-parameter zeroing neural network (ZNN) model, on the basis of the original varying parameter ZNN (VPZNN) model, an improved varying parameter ZNN (IVPZNN) model is established and researched to solve the time-varying matrix inversion (TVMI). Specifically, the value of the proposed novel time-varying parameter in the IVPZNN model can grow rapidly over time, which can better meet the needs of ZNN in hardware implementation. In addition, theoretical analyses of the novel time varying parameter and the proposed IVPZNN model are given to guarantee the global super-exponential convergence and finite-time convergence. Numerical calculation results verify the superior property of the established IVPZNN model for addressing the TVMI problem, as compared with the previously existing fixed-parameter ZNN and VPZNN models.