Finite-Time Solution of Time-Varying Tensor Inversion by a Novel Dynamic-Parameter Zeroing Neural-Network

Abstract

Time-varying tensor inversion (TVTI) problem is a kind of general time-varying inversion problem in mathematics because scalars, vectors and matrices can all be represented by tensors. The TVTI problem is based on a novel tensor product [termed the TensorFlow (TF) product], which is extracted from the TensorFlow. For solving such a prevalent problem, the matricization of the TF product is defined, and a novel dynamicparameter zeroing neural-network (DP-ZNN) model is proposed by combining a zeroing neural-network design formula and a dynamic-parameter. The global convergence and the upper bound of finite-time convergence of the DP-ZNN model are analyzed theoretically. For highlighting the superior convergence performance and excellent efficiency of the DP-ZNN model in solving the TVTI problem, three comparative experiments are presented in this paper. Experimental results show that the DP-ZNN model has remarkable convergent speciality.