Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/55727
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSahoo, Amit Kumar-
dc.contributor.authorPandey, Rudra Narayan-
dc.contributor.authorMishra, Sudhansu Kumar-
dc.contributor.authorDash, Prajna Parimita-
dc.date.accessioned2020-12-01T09:45:37Z-
dc.date.available2020-12-01T09:45:37Z-
dc.date.issued2020-12-
dc.identifier.issn0975-1084 (Online); 0022-4456 (Print)-
dc.identifier.urihttp://nopr.niscair.res.in/handle/123456789/55727-
dc.description1101-1105en_US
dc.description.abstractDeep neural network has emerged as one of the most effective networks for modeling of highly non-linear complex real-time systems. The long-short term memory network (LSTM) which is a one of the variants of recurrent neural network (RNN) has been proposed for the identification of a highly nonlinear Maglev plant. The comparative analysis of its performance is carried out with the functional link artificial neural network- least mean square (FLANN-LMS), FLANN-particle swarm optimization (FLANN-PSO), FLANN-teaching learning based optimization (FLANN-TLBO) and FLANN-black widow optimization (FLANN-BWO) algorithm. The proposed LSTM model is a feed forward neural network trained by a simple iterative method called the ADAM algorithm. The obtained results indicate that the proposed network has better performance than the other competitive networks in terms of the MSE, CPU time and convergence rate. To validate the dominance of the proposed network, a statistical tests, i.e. the Friedman test, is also applied.en_US
dc.language.isoen_USen_US
dc.publisherNISCAIR-CSIR, Indiaen_US
dc.rights CC Attribution-Noncommercial-No Derivative Works 2.5 Indiaen_US
dc.sourceJSIR Vol.79(12) [December 2020]en_US
dc.subjectFLANNen_US
dc.subjectMaglev systemen_US
dc.subjectMean Square Erroren_US
dc.subjectRecurrent Neural Networken_US
dc.subjectSystem identificationen_US
dc.titleIdentification of Real-Time Maglev Plant using Long-Short Term Memory Network based Deep Learning Techniqueen_US
dc.typeArticleen_US
Appears in Collections:JSIR Vol.79(12) [December 2020]

Files in This Item:
File Description SizeFormat 
JSIR 79(12) 1101-1105.pdf1.28 MBAdobe PDFView/Open


Items in NOPR are protected by copyright, with all rights reserved, unless otherwise indicated.