General recurrent neural network for solving generalized linear matrix equation
type: Item
instance of: scholarly article
title: General recurrent neural network for solving generalized linear matrix equation
zbMATH Open document ID: 1373.92011
DOI: 10.1155/2017/9063762
author: Zhan Li
author: Hong Cheng
author: Hongliang Guo
published in: Complexity
publication date: 2017-10-26T00:00:00Z
review text: Summary: This brief proposes a general framework of the nonlinear recurrent neural network for solving online the generalized linear matrix equation (GLME) with global convergence property. If the linear activation function is utilized, the neural state matrix of the nonlinear recurrent neural network can globally and exponentially converge to the unique theoretical solution of GLME. Additionally, as compared with the case of using the linear activation function, two specific types of nonlinear activation functions are proposed for the general nonlinear recurrent neural network model to achieve superior convergence. Illustrative examples are shown to demonstrate the efficacy of the general nonlinear recurrent neural network model and its superior convergence when activated by the aforementioned nonlinear activation functions.
Mathematics Subject Classification ID: 92B20
Mathematics Subject Classification ID: 15A24
Mathematics Subject Classification ID: 93C10
zbMATH DE Number: 6798521
zbMATH Keywords: nonlinear recurrent neural network
zbMATH Keywords: generalized linear matrix equation
zbMATH Keywords: global convergence
Wikidata QID: Q59142959
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/9063762
OpenAlex ID: W2740280874
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description: scientific article; zbMATH DE number 6798521