High-order Hopfield-based neural network for nonlinear system identification

Chi-Hsu Wang*, Kun Neng Hung

*Corresponding author for this work

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

1 Scopus citations

Abstract

The high-order Hopfield neural network (HOHNN) with functional link net has been developed in this paper for the purpose of system identification of nonlinear dynamical system. The weighting factors in HOHNN will be tuned via the Lyapunov stability criterion to guarantee the convergence performance of real-time system identification. In comparison with the traditional Hopfield neural network (HNN), the proposed architecture of HOHNN has additional inputs for each neuron which has the advantages of faster convergence rate and less computational load. The simulation results for both HNN and HOHNN are finally conducted to show the effectiveness of HOHNN in system identification of uncertain dynamical systems. It is obvious from the simulation results that the performance of system identification for HOHNN is better than that of HNN.

Original languageEnglish
Title of host publicationProceedings 2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009
Pages3346-3351
Number of pages6
DOIs
StatePublished - 1 Dec 2009
Event2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009 - San Antonio, TX, United States
Duration: 11 Oct 200914 Oct 2009

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)1062-922X

Conference

Conference2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009
CountryUnited States
CitySan Antonio, TX
Period11/10/0914/10/09

Keywords

  • Functional link net
  • Hopfield neural network
  • Lyapunov theorem

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    Wang, C-H., & Hung, K. N. (2009). High-order Hopfield-based neural network for nonlinear system identification. In Proceedings 2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009 (pp. 3346-3351). [5346190] (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics). https://doi.org/10.1109/ICSMC.2009.5346190