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

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.