An Efficient Method for Structural Identiability Analysis of Large Dynamic Systems
Paper i proceeding, 2012

Ordinary differential equation models often contain a large number of parameters that must be determined from measurements by parameter estimation. For a parameter estimation procedure to be successful, there must be a unique set of parameters that can have produced the measured data. This is not the case if a model is not structurally identifiable with the given set of outputs selected as measurements. We describe the implementation of a recent probabilistic semi-numerical method for testing local structural identifiability based on computing the rank of a numerically instantiated Jacobian matrix (observability/identifiability matrix). To obtain this, matrix parameters and initial conditions are specialized to random integer numbers, inputs are specialized to truncated random integer coefficient power series, and the corresponding output of the state space system is computed in terms of a truncated power series, which then is utilized to calculate the elements of a Jacobian matrix. To reduce the memory requirements and increase the speed of the computations all operations are done modulo a large prime number. The method has been extended to handle parametrized initial conditions and is demonstrated to be capable of handling systems in the order of a hundred state variables and equally many parameters on a standard desktop computer.

Identifiability

Nonlinear System Identification

Biological Systems

Författare

16th IFAC Symposium on System Identification

1474-6670 (ISSN)

Vol. 16 941-946

Ämneskategorier

Beräkningsmatematik

Systemvetenskap

Reglerteknik

Fundament

Grundläggande vetenskaper

Drivkrafter

Innovation och entreprenörskap

Styrkeområden

Livsvetenskaper och teknik

DOI

10.3182/20120711-3-BE-2027.00381

ISBN

978-3-902823-06-9