Approximation of state-space trajectories by locally recurrent globally feed-forward neural networks
type: Item
instance of: scholarly article
title: Approximation of state-space trajectories by locally recurrent globally feed-forward neural networks
zbMATH Open document ID: 1254.93107
author: Krzysztof Patan
published in: Neural Networks
publication date: 2013-01-17T00:00:00Z
Mathematics Subject Classification ID: 93C55
Mathematics Subject Classification ID: 92B20
Mathematics Subject Classification ID: 37N35
zbMATH DE Number: 6126248
zbMATH Keywords: recurrent networks
zbMATH Keywords: approximation ability
zbMATH Keywords: universal approximation theorem
zbMATH Keywords: state-space representation
zbMATH Keywords: Lipschitz mapping
zbMATH Keywords: cascade network
MaRDI profile type: Publication
full work available at URL: j.neunet.2007.10.004
OpenAlex ID: W2018698390
Wikidata QID: Q51897877
cites work: Learning of Chua's circuit attractors by locally recurrent neural networks
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cites work: Multilayer feedforward networks are universal approximators
cites work: Approximation of discrete-time state-space trajectories using dynamic recurrent neural networks
cites work: Q4700706
cites work: Q2704801
DOI: 10.1016/J.NEUNET.2007.10.004
DBLP publication ID: journals/nn/Patan08
description: scientific article; zbMATH DE number 6126248