Recurrent neural networks for LU decomposition and Cholesky factorization

J. Wang, G. Wu

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)

Abstract

Two recurrent neural networks are presented for LU decomposition and Cholesky factorization. The proposed recurrent neural networks consist of two bidirectionally connected layers and each layer consists of an array of neurons. The proposed recurrent neural networks are proven to be asymptotically stable in the large and capable of LU decomposition and Cholesky factorization.

Original languageEnglish
Pages (from-to)1-8
Number of pages8
JournalMathematical and Computer Modelling
Volume18
Issue number6
DOIs
Publication statusPublished - Sep 1993
Externally publishedYes

Fingerprint

Dive into the research topics of 'Recurrent neural networks for LU decomposition and Cholesky factorization'. Together they form a unique fingerprint.

Cite this