Journal
APPLIED MATHEMATICS AND COMPUTATION
Volume 215, Issue 3, Pages 1301-1306Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.amc.2009.06.048
Keywords
Gradient-based neural network; Online matrix inversion; Lyapunov stability theory; Asymptotical convergence; Global exponential convergence
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Funding
- National Science Foundation of China [60643004]
- Science and Technology Office of Sun Yat-Sen University
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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. (C) 2009 Elsevier Inc. All rights reserved.
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