A recursive identification algorithm for systems described by
nonlinear ordinary differential equation (ODE) models is proposed.
The ODE model is parameterized with coefficients of a polynomial
in the state variables and inputs, that describes one component of
the right hand side function of the ODE. This avoids
over-parameterization problems. The model is then discretized with
an Euler integration method. The algorithm exploits a Kalman
filter, where the state variables needed in the right hand side
function are derived by numerical differentiation. This approach
makes a standard Kalman filter applicable to the identification
problem. Contrary to a previously described RPEM algorithm, the
proposed Kalman filter scheme cannot converge to false local minima of the criterion function. The proposed algorithm is therefore suitable
for generation of initial values for the RPEM. The performance of
the Kalman filter based algorithm is illustrated using a numerical example.