Stochastic differential equations as a tool to regularize the parameter estimation problem for continuous time dynamical systems given discrete time measurements
Journal article, 2014

In this paper we consider the problem of estimating parameters in ordinary differential equations given discrete time experimental data. The impact of going from an ordinary to a stochastic differential equation setting is investigated as a tool to overcome the problem of local minima in the objective function. Using two different models, it is demonstrated that by allowing noise in the underlying model itself, the objective functions to be minimized in the parameter estimation procedures are regularized in the sense that the number of local minima is reduced and better convergence is achieved. The advantage of using stochastic differential equations is that the actual states in the model are predicted from data and this will allow the prediction to stay close to data even when the parameters in the model is incorrect. The extended Kalman filter is used as a state estimator and sensitivity equations are provided to give an accurate calculation of the gradient of the objective function. The method is illustrated using in silico data from the FitzHugh–Nagumo model for excitable media and the Lotka–Volterra predator–prey system. The proposed method performs well on the models considered, and is able to regularize the objective function in both models. This leads to parameter estimation problems with fewer local minima which can be solved by efficient gradient-based methods.

Stochastic differential equations

Ordinary differential equations

Parameter estimation

Lotka–Volterra

Extended Kalman filter

FitzHugh–Nagumo

Author

Jacob Leander

Chalmers, Mathematical Sciences, Mathematics

University of Gothenburg

Torbjörn Lundh

Chalmers, Mathematical Sciences, Mathematics

University of Gothenburg

Mats Jirstrand

Fraunhofer-Chalmers Centre

Mathematical Biosciences

0025-5564 (ISSN)

Vol. 251 1 54-62

Subject Categories

Computational Mathematics

Probability Theory and Statistics

Roots

Basic sciences

Areas of Advance

Life Science Engineering

DOI

10.1016/j.mbs.2014.03.001