An improved elman neural network controller based on quasi-ARX neural network for nonlinear systems

An improved Elman neural network (IENN) controller with particle swarm optimization (PSO) is presented for nonlinear systems. The proposed controller is composed of a quasi‐ARX neural network (QARXNN) prediction model and a switching mechanism. The switching mechanism is used to guarantee that the p...

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Bibliographic Details
Published in:IEEJ transactions on electrical and electronic engineering 2014-09, Vol.9 (5), p.494-501
Main Authors: Sutrisno, Imam, Abu Jami'in, Mohammad, Hu, Jinglu
Format: Article
Language:English
Subjects:
Computer simulation
Dynamical systems
Elman neural network
Learning
Lyapunov stability theorem
Mathematical models
Neural networks
Nonlinear dynamics
particle swarm optimization
quasi-ARX neural network
Swarm intelligence
Switching
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Summary:An improved Elman neural network (IENN) controller with particle swarm optimization (PSO) is presented for nonlinear systems. The proposed controller is composed of a quasi‐ARX neural network (QARXNN) prediction model and a switching mechanism. The switching mechanism is used to guarantee that the prediction model works well. The primary controller is designed based on IENN using the backpropagation (BP) learning algorithm with PSO. PSO is used to adjust the learning rates in the BP process for improving the learning capability. The adaptive learning rates of the controller are investigated via the Lyapunov stability theorem. The proposed controller performance is verified through numerical simulation. The method is compared with the fuzzy switching and 0/1 switching methods to show its effectiveness in terms of stability, accuracy, and robustness. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
ISSN:1931-4973
1931-4981
DOI:10.1002/tee.21998