A class of learning/estimation algorithms using nominal values: Asymptotic analysis and applications

被引：0
|
作者：
Yin, G [1 ]
Yin, K
Liu, B
Boukas, EK
机构：
[1] Wayne State Univ, Dept Math, Detroit, MI 48202 USA
[2] Univ Minnesota, Dept Wood & Paper Sci, St Paul, MN 55108 USA
[3] Coll St Scholastica, Dept Math, Duluth, MN USA
[4] Ecole Polytech, Dept Mech Engn, Montreal, PQ H3C 3A7, Canada
来源：
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS | 2000年 / 105卷 / 01期
基金：
加拿大自然科学与工程研究理事会; 美国国家科学基金会;
关键词：
learning; estimation; monitoring; industrial processes; kernel function; passive strategy; stochastic approximations;
D O I：
10.1023/A:1004622313930
中图分类号：
C93 [管理学]; O22 [运筹学];
学科分类号：
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要：
A class of estimation/learning algorithms using stochastic approximation in conjunction with two kernel functions is developed. This algorithm is recursive in form and uses known nominal values and other observed quantities. Its convergence analysis is carried out, the rate of convergence is also evaluated. Applications to a nonlinear chemical engineering system are examined through simulation study. The estimates obtained will be useful in process operation and control, and in on-line monitoring and fault detection.
引用
收藏
页码：189 / 212
页数：24
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