Abstract
In the identification of system whose a priori information of structure is poor, adequate candidate models are difficultly set up. This paper proposes the system identification method in which the object-oriented inference engine may be applied to search for the optimal model. The candidate models are generated one by one by using the search tree, and are evaluated by Bayesian theorem in consideration of the reliability of the a posteriori probability. Various simulation show that the model of true structure is discriminated clearly from the others as data increase. The system described by a functional expansion may be identified, because the flexible search of models may be possible on the basis of hierarchical inference.
