Recurrent neural networks for LU decomposition and Cholesky factorization

Authors:
J. Wang;G. Wu
Affiliations:
Department of Industrial Technology, University of North Dakota Grand Forks, ND 58202-7118, USA;Department of Industrial Technology, University of North Dakota Grand Forks, ND 58202-7118, USA
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
1993

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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.