Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/55727
Title: Identification of Real-Time Maglev Plant using Long-Short Term Memory Network based Deep Learning Technique
Authors: Sahoo, Amit Kumar
Pandey, Rudra Narayan
Mishra, Sudhansu Kumar
Dash, Prajna Parimita
Keywords: FLANN;Maglev system;Mean Square Error;Recurrent Neural Network;System identification
Issue Date: Dec-2020
Publisher: NISCAIR-CSIR, India
Abstract: Deep 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.
Page(s): 1101-1105
URI: http://nopr.niscair.res.in/handle/123456789/55727
ISSN: 0975-1084 (Online); 0022-4456 (Print)
Appears in Collections:JSIR Vol.79(12) [December 2020]

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