Optimal quasi-convex combinations for stochastic approximation algorithms with parallel observers
作者:
Y. M. Zhu,
G. Yin,
期刊:
International Journal of Control
(Taylor Available online 1989)
卷期:
Volume 49,
issue 6
页码: 1947-1964
ISSN:0020-7179
年代: 1989
DOI:10.1080/00207178908559754
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
Motivated by numerous potential applications in decentralized estimation, detection and adaptive control, Monte Carlo optimization, etc., two types of stochastic approximation (SA) algorithms with parallel observers are developed. To find the root of a non-linear function with random noise corrupted measurements, instead of employing the classical Robbins-Monro (RM) SA procedures with a single observer, a collection of physically separated parallel observers are used to estimate the same parameter. A newly formed approximation sequence is obtained by means of an appropriate quasi-convex combination of the most current values obtained from all the observers. In addition to getting strong consistency and asymptotic normality, the optimal convex combination coefficients are derived. Comparisons of asymptotic performance are made. These comparisons indicate that the algorithms suggested here are asymptotically better than the classical approach.
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