An Improved Elman Neural Network Controller Based on Quasi-ARX Neural Network for Nonlinear Systems

Sutrisno, Imam and Abu Jami’in, Mohammad and Hu, Jinglu (2014) An Improved Elman Neural Network Controller Based on Quasi-ARX Neural Network for Nonlinear Systems. IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING (9). pp. 494-501.

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Abstract

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.

Item Type: Article
Subjects: T Technology > T Technology (General)
Divisions: Jurusan Teknik Kelistrikan Kapal > Teknik Kelistrikan Kapal
Depositing User: Unnamed user with email repository@ppns.ac.id
Date Deposited: 22 Jan 2019 07:45
Last Modified: 28 Jan 2019 02:44
URI: http://repository.ppns.ac.id/id/eprint/1405

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