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<p>Identification of Real-Time Maglev Plant using Long-Short Term Memory network based Deep learning Technique</p>

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Title Statement <p>Identification of Real-Time Maglev Plant using Long-Short Term Memory network based Deep learning Technique</p>
 
Added Entry - Uncontrolled Name Mishra, Sudhansu Kumar; Dept.of EEE, Birla Institute of Technology,Mesra,Ranchi
Sahoo, Amit Kumar; Department of EEE, Centurion University of Technology and Management, Odisha, India
Pandey, Rudra Narayan; Dept.of EEE, Birla Institute of Technology,Mesra,Ranchi
Dash, Prajna Parimita; Department of ECE, BIT, Mesra,Ranchi
 
Uncontrolled Index Term System identification, Maglev system, FLANN, Mean Square Error, Recurrent Neural Network.
 
Summary, etc. <p>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.</p><p> </p>
 
Publication, Distribution, Etc. Journal of Scientific and Industrial Research (JSIR)
2021-01-11 11:19:19
 
Electronic Location and Access application/pdf
http://op.niscair.res.in/index.php/JSIR/article/view/38760
 
Data Source Entry Journal of Scientific and Industrial Research (JSIR); ##issue.vol## 79, ##issue.no## 12 (20)
 
Language Note en
 
Nonspecific Relationship Entry http://op.niscair.res.in/index.php/JSIR/article/download/38760/465520531