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An improved elman neural network controller based on quasi‐ARX neural network for nonlinear systems
Author(s) -
Sutrisno Imam,
Abu Jami'in Mohammad,
Hu Jinglu
Publication year - 2014
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.21998
Subject(s) - artificial neural network , control theory (sociology) , particle swarm optimization , robustness (evolution) , backpropagation , nonlinear system , computer science , controller (irrigation) , lyapunov stability , control engineering , engineering , artificial intelligence , machine learning , control (management) , biology , gene , biochemistry , chemistry , physics , quantum mechanics , agronomy
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.