Recurrent neural networks for dynamic system modeling

Jennie Si, Liguang Pang

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

3 Scopus citations

Abstract

Stability properties of recurrent neural networks are investigated using Lyapunov stability theory. Two sufficient conditions for the global asymptotic stability of equilibrium points of a class of recurrent neural networks are provided. Applicability of recurrent neural networks for nonlinear dynamic system modeling and control is discussed.

Original languageEnglish (US)
Title of host publicationProc 1993 IEEE Int Symp Intell Control
PublisherPubl by IEEE
Pages364-369
Number of pages6
ISBN (Print)0780312074
StatePublished - Dec 1 1993
EventProceedings of the 1993 IEEE International Symposium on Intelligent Control - Chicago, IL, USA
Duration: Aug 25 1993Aug 27 1993

Publication series

NameProc 1993 IEEE Int Symp Intell Control

Other

OtherProceedings of the 1993 IEEE International Symposium on Intelligent Control
CityChicago, IL, USA
Period8/25/938/27/93

    Fingerprint

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Si, J., & Pang, L. (1993). Recurrent neural networks for dynamic system modeling. In Proc 1993 IEEE Int Symp Intell Control (pp. 364-369). (Proc 1993 IEEE Int Symp Intell Control). Publ by IEEE.