Berlin 2012 – scientific programme

Parts | Days | Selection | Search | Updates | Downloads | Help

DY: Fachverband Dynamik und Statistische Physik

DY 28: Data Analysis Methods and Modelling of Geophysical Systems

DY 28.5: Talk

Thursday, March 29, 2012, 16:00–16:15, MA 144

State and parameter estimation for nonlinear systems — •Jan Schumann-Bischoff, Stefan Luther, and Ulrich Parlitz — Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Am Fassberg 17, 37077 Göttingen

We present an efficient method for estimating variables and parameters of a given system of ordinary differential equations by adapting the model output to an observed time series from the (physical) process described by the model. The proposed method [1] is based on (unconstrained) nonlinear optimization exploiting the particular structure of the relevant cost function. For illustrating features and performance of the method simulations are presented using chaotic time series generated by the Colpitts oscillator, the three dimensional Hindmarsh-Rose neuron model and a 9-dimensional extended hyperchaotic Rössler system.
J. Schumann-Bischoff and U. Parlitz, State and parameter estimation using unconstrained optimization, Phys. Rev. E 84, 056214 (2011)