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

Imam Sutrisno, Mohammad Abu Jami'in, Jinglu Hu*

*この研究の対応する著者

研究成果: Article査読

8 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)494-501
ページ数8
ジャーナルIEEJ Transactions on Electrical and Electronic Engineering
9
5
DOI
出版ステータスPublished - 2014 9

ASJC Scopus subject areas

  • 電子工学および電気工学

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