Recurrent neural network for computing the W-weighted Drazin inverse

Xue-Zhong Wang, Haifeng Ma and Predrag S. Stanimirović

Applied Mathematics and Computation, 2017, vol. 300, issue C, 1-20

Abstract: Two gradient based recurrent neural networks (RNNs) for computing the W-weighted Drazin inverse of a real rectangular matrix are proposed and considered. Usage of the first RNN is limited by a specific constraint on the spectrum of a certain matrix. The second RNN is usable without restrictions. The stability of the recurrent neural networks as well as their convergence are considered. Numerical examples are given to show the efficiency of the proposed neural networks.

Keywords: Recurrent neural network; W-weighted Drazin inverse; Dynamic equation (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0096300316307068
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:300:y:2017:i:c:p:1-20

DOI: 10.1016/j.amc.2016.11.030

Access Statistics for this article

Applied Mathematics and Computation is currently edited by Theodore Simos

More articles in Applied Mathematics and Computation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().