Recurrent neural networks for LU decomposition and Cholesky factorization

J. Wang; G. Wu

doi:10.1016/0895-7177(93)90121-E

Recurrent neural networks for LU decomposition and Cholesky factorization

J. Wang
, G. Wu

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

23 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. © 1993.

Original language	English
Pages (from-to)	1-8
Journal	Mathematical and Computer Modelling
Volume	18
Issue number	6
DOIs	https://doi.org/10.1016/0895-7177(93)90121-E
Publication status	Published - Sept 1993
Externally published	Yes

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title = "Recurrent neural networks for LU decomposition and Cholesky factorization",

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. {\textcopyright} 1993.",

author = "J. Wang and G. Wu",

year = "1993",

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doi = "10.1016/0895-7177(93)90121-E",

language = "English",

volume = "18",

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TY - JOUR

T1 - Recurrent neural networks for LU decomposition and Cholesky factorization

AU - Wang, J.

AU - Wu, G.

PY - 1993/9

Y1 - 1993/9

N2 - 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. © 1993.

AB - 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. © 1993.

UR - https://www.scopus.com/pages/publications/38248999254

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-38248999254&origin=recordpage

U2 - 10.1016/0895-7177(93)90121-E

DO - 10.1016/0895-7177(93)90121-E

M3 - RGC 21 - Publication in refereed journal

SN - 0895-7177

VL - 18

SP - 1

EP - 8

JO - Mathematical and Computer Modelling

JF - Mathematical and Computer Modelling

IS - 6

ER -

Recurrent neural networks for LU decomposition and Cholesky factorization

Abstract

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