A Class of Learning/Estimation Algorithms Using Nominal Values: Asymptotic Analysis and Applications

G. Yin, K. Yin, B. Liu and E. K. Boukas
Additional contact information
G. Yin: Wayne State University
K. Yin: University of Minnesota
B. Liu: College of Saint Scholastica
E. K. Boukas: École Polytechnique de Montréal

Journal of Optimization Theory and Applications, 2000, vol. 105, issue 1, No 10, 189-212

Abstract: Abstract 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.

Keywords: learning; estimation; monitoring; industrial processes; kernel function; passive strategy; stochastic approximations (search for similar items in EconPapers)
Date: 2000
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DOI: 10.1023/A:1004622313930

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