Zeroing Neural Network for Pseudoinversion of an Arbitrary Time-Varying Matrix Based on Singular Value Decomposition

Many researchers have investigated the time-varying (TV) matrix pseudoinverse problem in recent years, for its importance addressing TV problems science and engineering. In this paper, of calculating inverse or an arbitrary real is considered addressed using singular value decomposition (SVD) zeroing neural network (ZNN) approaches. Since SVD frequently used to compute a matrix, research proposes new ZNN model based on method as well technique Tikhonov regularization, solving continuous time. Numerical experiments, involving pseudoinversion square, rectangular, singular, nonsingular input matrices, indicate that proposed models are effective inversion time varying matrices.