Parameter estimation for non-linear continuous-time systems in a bounded error context

This paper deals with guaranteed parameter estimation in a bounded error context for nonlinear continuous-time systems. Perturbations are assumed bounded but otherwise unknown. The solution is a set of parameter vectors consistent with modelling hypotheses, measured data and prior error bounds. The algorithm proposed in this paper does not suffer from initialization problems encountered in the local optimization methods. The main tools to solve such a problem are the guaranteed techniques for the integration of ordinary differential equations, and set inversion. Both of these techniques use interval analysis. An illustrative example is given.