Multi-parameter identification from scalar time series generated by a Malkus-Lorenz water wheel.

Published

Journal Article

We address the issue of multi-parameter estimation from scalar outputs of chaotic systems, using the dynamics of a Malkus water wheel and simulations of the corresponding Lorenz-equations model as an example. We discuss and compare two estimators: one is based on a globally convergent adaptive observer and the second is an extended Kalman filter (EKF). Both estimators can identify all three unknown parameters of the model. We find that the estimated parameter values are in agreement with those obtained from direct measurements on the experimental system. In addition, we explore the question of how to distinguish the impact of noise from those of model imperfections by investigating a model generalization and the use of uncertainty estimates provided by the extended Kalman filter. Although we are able to exclude asymmetric inflow as a possible unmodeled effect, our results indicate that the Lorenz-equations do not perfectly describe the water wheel dynamics.

Full Text

Duke Authors

Cited Authors

  • Illing, L; Saunders, AM; Hahs, D

Published Date

  • March 2012

Published In

Volume / Issue

  • 22 / 1

Start / End Page

  • 013127 -

PubMed ID

  • 22463003

Pubmed Central ID

  • 22463003

Electronic International Standard Serial Number (EISSN)

  • 1089-7682

Digital Object Identifier (DOI)

  • 10.1063/1.3689441

Language

  • eng

Conference Location

  • United States