Modeling Experimental Time Series with Ordinary Differential Equations

Abstract

Recently some methods have been presented to extract ordinary differential equations (ODE) directly from an experimental time series. Here, we introduce a new method to find an ODE which models both the short time and the long time dynamics. The experimental data are represented in a state space and the corresponding flow vectors are approximated by polynomials of the state vector components. We apply these methods both to simulated data and experimental data from human limb movements, which like many other biological systems can exhibit limit cycle dynamics. In systems with only one oscillator there is excellent agreement between the limit cycling displayed by the experimental system and the reconstructed model, even if the data are very noisy. Furthermore we study systems of two coupled limit cycle oscillators. There, a reconstruction was only successful for data with a sufficiently long transient trajectory and relatively low noise level.

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Document Details

Document Type
Technical Report
Publication Date
Aug 06, 1989
Accession Number
ADA245831

Entities

People

  • A. Huebler
  • J. A. Kelso
  • N. Packard
  • T. Eisenhammer

Organizations

  • University of Illinois Urbana–Champaign

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Complex Systems
  • Computational Science
  • Differential Equations
  • Equations
  • Experimental Data
  • Fluid Mechanics
  • Nervous System
  • Noise
  • Nonlinear Dynamics
  • Oscillation
  • Oscillators
  • Phase Transformations
  • Physics
  • Relaxation Time
  • Simulations
  • Systems Biology
  • Two Dimensional

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computational Modeling and Simulation
  • Control Systems Engineering.

Technology Areas

  • Space