Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4633457 | Applied Mathematics and Computation | 2009 | 6 Pages |
Abstract
Wang proposed a gradient-based neural network (GNN) to solve online matrix-inverses. Global asymptotical convergence was shown for such a neural network when applied to inverting nonsingular matrices. As compared to the previously-presented asymptotical convergence, this paper investigates more desirable properties of the gradient-based neural network; e.g., global exponential convergence for nonsingular matrix inversion, and global stability even for the singular-matrix case. Illustrative simulation results further demonstrate the theoretical analysis of gradient-based neural network for online matrix inversion.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Yunong Zhang, Yanyan Shi, Ke Chen, Chaoli Wang,