A Neural Network for Moore–Penrose Inverse of Time-Varying Complex-Valued Matrices
Yiyuan Chai, Haojin Li, Defeng Qiao, Sitian Qin, Jiqiang Feng
Abstract
The Moore-Penrose inverse of a matrix plays a very important role in practical applications.In general, it is not easy to immediately solve the Moore-Penrose inverse of a matrix, especially for solving the Moore-Penrose inverse of a complex-valued matrix in time-varying situations.To solve this problem conveniently, in this paper, a novel Zhang neural network (ZNN) with timevarying parameter that accelerates convergence is proposed, which can solve Moore-Penrose inverse of a matrix over complex field in real time.Analysis results show that the state solutions of the proposed model can achieve super convergence in finite time with weighted sign-bi-power activation function (WSBP) and the upper bound of the convergence time is calculated.A related noise-tolerance model which possesses finite-time convergence property is proved to be more efficient in noise suppression.At last, numerical simulation illustrates the performance of the proposed model as well.