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Identification of Real-Time Maglev Plant using Long-Short Term Memory Network based Deep Learning Technique

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Title Identification of Real-Time Maglev Plant using Long-Short Term Memory Network based Deep Learning Technique
 
Creator Sahoo, Amit Kumar
Pandey, Rudra Narayan
Mishra, Sudhansu Kumar
Dash, Prajna Parimita
 
Subject FLANN
Maglev system
Mean Square Error
Recurrent Neural Network
System identification
 
Description 1101-1105
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.
 
Date 2020-12-01T09:45:37Z
2020-12-01T09:45:37Z
2020-12
 
Type Article
 
Identifier 0975-1084 (Online); 0022-4456 (Print)
http://nopr.niscair.res.in/handle/123456789/55727
 
Language en_US
 
Rights CC Attribution-Noncommercial-No Derivative Works 2.5 India
 
Publisher NISCAIR-CSIR, India
 
Source JSIR Vol.79(12) [December 2020]