Recurrent neural networks for solving linear matrix equations
type: Item
instance of: scholarly article
title: Recurrent neural networks for solving linear matrix equations
zbMATH Open document ID: 0796.65058
DOI: 10.1016/0898-1221(93)90003-E
publication date: 1994-10-10T00:00:00Z
review text: The author describes how recurrent neural networks can be used for solving linear matrix equations. He shows that the methods are asymptotically stable in the large and can be used for solving Lyapunov equations. He gives two examples for handling continuous-time and discrete-time Lyapuov equations. The author also shows the relationship between the Lyapunov function and the activation function. The proposed recurrent neural networks are better than the supervised learning approach, since prior training is unnecessary.
Mathematics Subject Classification ID: 65F30
Mathematics Subject Classification ID: 65K10
Mathematics Subject Classification ID: 15A24
Mathematics Subject Classification ID: 93C55
zbMATH DE Number: 487969
zbMATH Keywords: neural networks
zbMATH Keywords: linear matrix equations
zbMATH Keywords: Lyapunov equations
zbMATH Keywords: continuous- time
zbMATH Keywords: discrete-time
zbMATH Keywords: activation function
zbMATH Keywords: continuous-time
author: Jun Wang
reviewed by: R. P. Tewarson
MaRDI profile type: Publication
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published in: Computers & Mathematics with Applications
description: scientific article; zbMATH DE number 487969