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

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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.

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Authors and Affiliations

  1. Department of Mathematics, Wayne State University, Detroit, Michigan

    G. Yin (Professor)

  2. Department of Wood and Paper Science, University of Minnesota, Saint Paul, Minnesota

    K. Yin (Associate Professor)

  3. Department of Mathematics, College of Saint Scholastica, Duluth, Minnesota

    B. Liu (Associate Professor)

  4. Mechanical Engineering Department, École Polytechnique de Montréal, Montréal, Québec, Canada

    E. K. Boukas (Professor)

Authors
  1. G. Yin
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  2. K. Yin
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  3. B. Liu
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  4. E. K. Boukas
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Yin, G., Yin, K., Liu, B. et al. A Class of Learning/Estimation Algorithms Using Nominal Values: Asymptotic Analysis and Applications. Journal of Optimization Theory and Applications 105, 189–212 (2000). https://doi.org/10.1023/A:1004622313930

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  • DOI: https://doi.org/10.1023/A:1004622313930

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