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

Imam Sutrisno, Mohammad Abu Jami'in, Jinglu Hu

Research output: Contribution to journalArticle

7 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)494-501
Number of pages8
JournalIEEJ Transactions on Electrical and Electronic Engineering
Volume9
Issue number5
DOIs
Publication statusPublished - 2014 Sep

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Keywords

  • Elman neural network
  • Lyapunov stability theorem
  • Particle swarm optimization
  • Quasi-ARX neural network

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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