DocumentCode
490289
Title
Inductive Inference of Invariant Subspaces
Author
Lemmon, Michael
Author_Institution
Department of Electrical Engineering, University of Notre Dame, Notre Dame, Indiana 46556
fYear
1993
fDate
2-4 June 1993
Firstpage
1219
Lastpage
1223
Abstract
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.
Keywords
Convergence; Eigenvalues and eigenfunctions; Equations; Inference algorithms; Iterative algorithms; Machine learning algorithms; Protocols; Symmetric matrices; Variable structure systems; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
American Control Conference, 1993
Conference_Location
San Francisco, CA, USA
Print_ISBN
0-7803-0860-3
Type
conf
Filename
4793062
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