Communicable knowledge in automated system identification

Abstract

We describe the program PRET, an engineering tool for non-linear system identification, which is the task of inferring a (possibly nonlinear) ordinary differential equation model from external observations of a target system's behavior. PRET has several characteristics in common with programs from the fields of machine learning and computational scientific discovery. However, since PRET is intended to be an engineer's tool, it makes different choices with regard to the tradeoff between model accuracy and parsimony. The choice of a good model depends on the engineering task at hand, and PRET is designed to let the user communicate the task-specific modeling constraints to the program. PRET'S inputs, its outputs, and its internal knowledge base are instances of communicable knowledge-knowledge that is represented in a form that is meaningful to the domain experts that are the intended users of the program. © Springer-Verlag Berlin Heidelberg 2007.