A novel method to identify nonlinear dynamic systems

Ching Hung Lee, Ching Cheng Teng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper presents a new method for identifying a nonlinear system using the Hammerstein model. Such model consists of static nonlinear part and linear dynamic part in a cascading structure. The static nonlinear part is modeled by a fuzzy neural network (FNN), and the linear dynamic part is modeled by an auto-regressive moving average (ARMA) model. Based on our approach, a nonlinear dynamical system can be divided into two parts, a nonlinear static function and an ARMA model. Furthermore, a simple learning algorithm is developed for obtaining the parameters of FNN and ARMA model. In addition, the convergence analysis for the cascade model (FNN+ARMA) is also studied by the Lyapunov approach. A simulation result is given to illustrate the effectiveness of the proposed method. Simulation result also demonstrates that this approach is useful for systems with disturbance input.

Original languageEnglish
Title of host publicationEuropean Control Conference, ECC 1999 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2530-2535
Number of pages6
ISBN (Electronic)9783952417355
DOIs
StatePublished - 24 Mar 2015
Event1999 European Control Conference, ECC 1999 - Karlsruhe, Germany
Duration: 31 Aug 19993 Sep 1999

Publication series

NameEuropean Control Conference, ECC 1999 - Conference Proceedings

Conference

Conference1999 European Control Conference, ECC 1999
Country/TerritoryGermany
CityKarlsruhe
Period31/08/993/09/99

Keywords

  • Fuzzy neural network
  • Hammerstein model
  • Identification
  • Nonlinear systems

Fingerprint

Dive into the research topics of 'A novel method to identify nonlinear dynamic systems'. Together they form a unique fingerprint.

Cite this