Neural network approach for solving nonsingular multi-linear tensor systems

Xuezhong Wang, Maolin Che, Yimin Wei

Abstract: The main propose of this paper is to develop two neural network models for solving nonsingular multi-linear tensor system. Theoretical analysis shows that each of the neural network models ensures the convergence performance. For possible hardware implementation of the proposed neural network models, based on digital circuits, we adopt the Euler-type difference rule to discretize the corresponding Gradient neural network (GNN) models. The computer simulation results further substantiate that the models can solve a multi-linear system with nonsingular tensors.