Lyapunov learning algorithm for Quasi-ARX neural network to identification of nonlinear dynamical system

Mohammad Abu Jami'in, Imam Sutrisno, Takayuki Furuzuki

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

9 Citations (Scopus)

Abstract

In this note, we present the modeling of nonlinear dynamical systems with Quasi-ARX neural network using Lyapunov algorithm in learning process. This work exploits the idea on learning algorithm in nonlinear kernel part of Quasi-ARX model to improve stability and fast convergence of error. The proposed algorithm is then employed to model and predict a classical nonlinear system with input dead zone and nonlinear dynamic systems, exhibiting the effectiveness of proposed algorithm. Based on the result of simulation, the proposed algorithm can make the error in process learning become fast convergence, ultimately bounded, and the error distributed uniformly.

Original languageEnglish
Title of host publicationConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Pages3147-3152
Number of pages6
DOIs
Publication statusPublished - 2012
Event2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012 - Seoul
Duration: 2012 Oct 142012 Oct 17

Other

Other2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
CitySeoul
Period12/10/1412/10/17

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Keywords

  • Lyapunov
  • modeling
  • Neural network
  • nonlinear
  • Quasi-ARX model
  • stability

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction

Cite this

Jami'in, M. A., Sutrisno, I., & Furuzuki, T. (2012). Lyapunov learning algorithm for Quasi-ARX neural network to identification of nonlinear dynamical system. In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics (pp. 3147-3152). [6378275] https://doi.org/10.1109/ICSMC.2012.6378275