ABSTRACT

Dynamical models explain how a system responds to a change in the system itself or in its environment. They are usually expressed as a set of differential equations that explicitly link change as defined by time derivatives to the state of the system and its inputs. Nonlinear systems are especially interesting because of their potential to capture complex system behavior by a seemingly simple set of equations. A new method will be outlined which permits the estimation of nonlinear system parameters from noisy data along with confidence regions and other inferential methodology. The procedure will be illustrated with examples drawn from chemical engineering, clinical medicine and ecology.