  The combination of the multiple shooting strategy with the generalized
Gauss-Newton algorithm turns out in a recognized method for estimating
parameters in ordinary differential equations (ODEs) from noisy discrete
observations. A key issue for an efficient implementation of this method is the
accurate integration of the ODE and the evaluation of the derivatives involved
in the optimization algorithm. In this paper, we study the feasibility of the
Local Linearization (LL) approach for the simultaneous numerical integration of
the ODE and the evaluation of such derivatives. This integration approach
results in a stable method for the accurate approximation of the derivatives
with no more computational cost than the that involved in the integration of
the ODE. The numerical simulations show that the proposed Multiple
Shooting-Local Linearization method recovers the true parameters value under
different scenarios of noisy data.
