Inductive Inference of Invariant Subspaces
Michael D. Lemmon · 1993
This paper shows that inductive inference protocols can learn invariant linear subspaces, used in the stabilization of variable structure systems, after a finite number of failed oracle queries. It is further shown that this convergence bound scales in a polynomial manner with the system's state space dimension.