A Novel-Restraint and Predefined-Time ZNN Model for Time-Dependent Matrix Inversion

The problem of solving complex-valued time-dependent matrix inversion (CVTD-MI) has significant implications for statistics and control theory, and thus it is worthy to be investigated by scholars. Zeroing neural network (ZNN) is an efficient tool for solving time-dependent problems and can be used to get the inverse of complex-variable matrix in disturbed environments. In this paper, a complex-valued fast predefined zeroing neural network (CFPZNN) model is proposed for solving the CVTD-MI problem. The CFPZNN model possesses two considerable properties. In the first place, the predefined time convergence performance ensures that the CFPZNN model can solve the CVTD-MI problems efficiently. In the second place, the CFPZNN model has suprior robustness and can restrain the interference of noise. These two superior points are demonstrated through several sets of numerical simulations. Moreover, the effectiveness and advantages of the proposed CFPZNN model are further demonstrated by citing the widely applied activation functions for comparison.