Estimation of states and parameters in chaotic systems using particle swarm optimization

B. Samanta, C. Nataraj

Research output: Contribution to book or proceedingConference articlepeer-review

Abstract

A study is presented on the application of particle swarm optimization (PSO) for estimation of states and parameters in chaotic systems. The parameter estimation is formulated as a nonlinear optimization problem using PSO to minimize the synchronization error for the observable states of the actual system and its mathematical model. The procedure is illustrated using a typical chaotic system of Lorenz equations. Results are presented to study the effects of different observable system states and observation noise on parameter estimation. The effectiveness of different variants of PSO on parameter estimation is also studied. The results show the capability of the proposed PSO based approach in estimating the chaotic system parameters with wide search range (50-200%) and in the presence of observation noise.

Original languageEnglish
Title of host publicationProceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference 2009, DETC2009
Pages651-659
Number of pages9
EditionPART A
DOIs
StatePublished - 2010
Event2009 ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, DETC2009 - San Diego, CA, United States
Duration: Aug 30 2009Sep 2 2009

Publication series

NameProceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference 2009, DETC2009
NumberPART A
Volume1

Conference

Conference2009 ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, DETC2009
Country/TerritoryUnited States
CitySan Diego, CA
Period08/30/0909/2/09

Keywords

  • Chaotic systems
  • Parameter estimation
  • Swarm intelligence
  • Swarm optimization
  • Time series synchronization

Fingerprint

Dive into the research topics of 'Estimation of states and parameters in chaotic systems using particle swarm optimization'. Together they form a unique fingerprint.

Cite this