DocumentCode
2852407
Title
On equivalence of some noise conditions for stochastic approximation algorithms
Author
Wang, I-Jeng ; Chong, Edwin K P ; Kulkarni, Sanjeev R.
Author_Institution
Sch. of Electr. & Comput. Eng., Purdue Univ., West Lafayette, IN, USA
Volume
4
fYear
1995
fDate
13-15 Dec 1995
Firstpage
3849
Abstract
We study four conditions on noise sequences for convergence of stochastic approximation algorithms on a general Hilbert space: Kushner and Clark´s condition (1978), Chen´s condition (1994), Kulkarni and Horn´s condition (1995), and a decomposition condition. We discuss various properties of these conditions. In our main result we show that the four conditions are all equivalent, and are both necessary and sufficient for convergence of stochastic approximation algorithms under appropriate assumptions
Keywords
Hilbert spaces; approximation theory; noise; convergence; decomposition condition; general Hilbert space; necessary and sufficient conditions; noise condition equivalence; stochastic approximation algorithms; Adaptive control; Approximation algorithms; Books; Convergence; Hilbert space; Stochastic processes; Stochastic resonance; Sufficient conditions;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 1995., Proceedings of the 34th IEEE Conference on
Conference_Location
New Orleans, LA
ISSN
0191-2216
Print_ISBN
0-7803-2685-7
Type
conf
DOI
10.1109/CDC.1995.479198
Filename
479198
Link To Document