Seminar Details
2014-05-13 (14.00) : Analytic interpolation and experiment design in system identification
At Euler building
Organized by Mathematical Engineering
Speaker :
Kaushik Mahata (University of Newcastle, Australia)
Abstract :
We investigate how the variance error associated with the prediction
error identification is related to the power spectral densities of the input and the
additive noise at the output. We characterize the set of all input and noise power
spectral densities for which the variance error remains constant. This analysis
results a minimal, finite-dimensional, affine parameterization of the variance error.
This parameterization connects our analysis with the theory of Nevanlinna-Pick
interpolation. It is shown that the set of all PSDs for which the variance error
remains constant can be characterized by the solutions of a Nevanlinna-Pick
interpolation problem. This insight has interesting consequences in optimal input
design, where it is possible to use some recent tools in analytic interpolation theory
to tune shape of the input PSD to suit certain needs.