title: Complex Neural Network Models for Time-Varying Drazin Inverse.
abstract: Two complex Zhang neural network (ZNN) models for computing the Drazin inverse of arbitrary time-varying complex square matrix are presented. The design of these neural networks is based on corresponding matrix-valued error functions arising from the limit representations of the Drazin inverse. Two types of activation functions, appropriate for handling complex matrices, are exploited to develop each of these networks. Theoretical results of convergence analysis are presented to show the desirable properties of the proposed complex-valued ZNN models. Numerical results further demonstrate the effectiveness of the proposed models.
authors.affiliation: School of Mathematical Sciences, Fudan University, Shanghai, 200433, P.R.C. 14130180001@fudan.edu.cn.
authors.forename: Xue-Zhong
authors.initials: XZ
authors.lastname: Wang
authors.affiliation: School of Mathematical Sciences and Key Laboratory of Mathematics for Nonlinear Sciences, Fudan University, Shanghai, 200433, P.R.C. ymwei@fudan.edu.cn.
authors.forename: Yimin
authors.initials: Y
authors.lastname: Wei
authors.affiliation: University of Niš, Faculty of Sciences and Mathematics, 18000 Niš, Serbia pecko@pmf.ni.ac.rs.
authors.forename: Predrag S
authors.initials: PS
authors.lastname: Stanimirović
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D016571:Neural Networks, Computer