A parallel computing method based on zeroing neural networks for time-varying complex-valued matrix Moore-Penrose inversion.

Abstract This paper analyzes the existing zeroing neural network (ZNN) models from the perspective of control theory. It proposes an exclusive ZNN model for solving the dynamic complex-valued matrix Moore-Penrose inverse problem: the complex-valued zeroing neural network (CVZNN). Then, a method of constructing a special type of saturation-allowed activation function is defined, which relaxes the convex constraint on the activation function when constructing the ZNN model. The convergence of the CVZNN model activated by proposed saturation-allowed functions is analyzed. Besides, the robustness of the CVZNN model under different types of noise interference is investigated based on the perspective of the control theory. Finally, the effectiveness and superiority of the CVZNN model are illustrated by simulation experiments.