Title :
An alternative proof for convergence of stochastic approximation algorithms
Author :
Kulkarni, S.R. ; Horn, C.S.
Author_Institution :
Dept. of Electr. Eng., Princeton Univ., NJ, USA
fDate :
3/1/1996 12:00:00 AM
Abstract :
An alternative proof for convergence of stochastic approximation algorithms is provided. The proof is completely deterministic, very elementary (involving only basic notions of convergence), and direct in that it remains in a discrete setting. An alternative form of the Kushner-Clark condition is introduced and utilized and the results are the first to prove necessity for general gain sequences in a Hilbert space setting
Keywords :
Hilbert spaces; approximation theory; convergence of numerical methods; Hilbert space; convergence; deterministic proof; general gain sequences; stochastic approximation algorithms; Adaptive control; Algorithm design and analysis; Approximation algorithms; Convergence; Differential equations; Hilbert space; Stochastic processes; Stochastic resonance; Stochastic systems; System identification;
Journal_Title :
Automatic Control, IEEE Transactions on
