Recurrent neural networks for dynamic system modeling

Jennie Si, Liguang Pang

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

3 Citations (Scopus)

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
Place of PublicationPiscataway, NJ, United States
PublisherPubl by IEEE
Pages364-369
Number of pages6
ISBN (Print)0780312074
StatePublished - 1993
EventProceedings of the 1993 IEEE International Symposium on Intelligent Control - Chicago, IL, USA
Duration: Aug 25 1993Aug 27 1993

Other

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

Fingerprint

Recurrent neural networks
Dynamical systems
Asymptotic stability

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). Piscataway, NJ, United States: Publ by IEEE.

Recurrent neural networks for dynamic system modeling. / Si, Jennie; Pang, Liguang.

Proc 1993 IEEE Int Symp Intell Control. Piscataway, NJ, United States : Publ by IEEE, 1993. p. 364-369.

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

Si, J & Pang, L 1993, Recurrent neural networks for dynamic system modeling. in Proc 1993 IEEE Int Symp Intell Control. Publ by IEEE, Piscataway, NJ, United States, pp. 364-369, Proceedings of the 1993 IEEE International Symposium on Intelligent Control, Chicago, IL, USA, 8/25/93.
Si J, Pang L. Recurrent neural networks for dynamic system modeling. In Proc 1993 IEEE Int Symp Intell Control. Piscataway, NJ, United States: Publ by IEEE. 1993. p. 364-369
Si, Jennie ; Pang, Liguang. / Recurrent neural networks for dynamic system modeling. Proc 1993 IEEE Int Symp Intell Control. Piscataway, NJ, United States : Publ by IEEE, 1993. pp. 364-369
@inproceedings{4a5b10ea9fdc41d991e8d7d03add099b,
title = "Recurrent neural networks for dynamic system modeling",
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.",
author = "Jennie Si and Liguang Pang",
year = "1993",
language = "English (US)",
isbn = "0780312074",
pages = "364--369",
booktitle = "Proc 1993 IEEE Int Symp Intell Control",
publisher = "Publ by IEEE",

}

TY - GEN

T1 - Recurrent neural networks for dynamic system modeling

AU - Si, Jennie

AU - Pang, Liguang

PY - 1993

Y1 - 1993

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0027805390&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0027805390&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0027805390

SN - 0780312074

SP - 364

EP - 369

BT - Proc 1993 IEEE Int Symp Intell Control

PB - Publ by IEEE

CY - Piscataway, NJ, United States

ER -