Self-organizing quasi-linear ARX RBFN modeling for identification and control of nonlinear systems

Imam Sutrisno, Mohammad Abu Jami'In, Takayuki Furuzuki, Mohammad Hamiruce Marhaban

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The quasi-linear ARX radial basis function network (QARX-RBFN) model has shown good approximation ability and usefulness in nonlinear system identification and control. It owns an ARX-like structure, easy design, good generalization and strong tolerance to input noise. However, the QARX-RBFN model still needs to improve the prediction accuracy by optimizing its structure. In this paper, a novel self-organizing QARX-RBFN (SOQARX-RBFN) model is proposed to solve this problem. The proposed SOQARX-RBFN model consists of simultaneously network construction and parameter optimization. It offers two important advantages. Firstly, the hidden neurons in the SOQARX-RBFN model can be added or removed, based on the neuron activity and mutual information (MI), to achieve the appropriate network complexity and maintain overall computational efficiency for identification. Secondly, the model performance can be significantly improved through the structure optimization. Additionally, the convergence of the SOQARX-RBFN model is analyzed, and the proposed approach is applied to identify and control the nonlinear dynamical systems. Mathematical system simulations are carried out to demonstrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publication2015 54th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages642-647
Number of pages6
ISBN (Print)9784907764487
DOIs
Publication statusPublished - 2015 Sep 30
Event54th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2015 - Hangzhou, China
Duration: 2015 Jul 282015 Jul 30

Other

Other54th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2015
CountryChina
CityHangzhou
Period15/7/2815/7/30

Keywords

  • artificial neural network
  • identification system
  • radial basis function network
  • self-organization

ASJC Scopus subject areas

  • Control and Systems Engineering

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    Sutrisno, I., Jami'In, M. A., Furuzuki, T., & Marhaban, M. H. (2015). Self-organizing quasi-linear ARX RBFN modeling for identification and control of nonlinear systems. In 2015 54th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2015 (pp. 642-647). [7285332] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SICE.2015.7285332