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A short note on SPSA techniques and their use in nonlinear bioprocess identification

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  • A. Vande Wouwer
  • C. Renotte
  • Ph. Bogaerts

Abstract

Simultaneous perturbation stochastic approximation (SPSA) is a gradient-based optimization method which has become popular since the 1990s. In contrast with standard numerical procedures, this method requires only a few cost function evaluations to obtain gradient information, and can therefore be advantageously applied when identifying a large number of unknown model parameters, as for instance in neural network models or first-principles models. In this paper, a first-order SPSA algorithm is introduced, which makes use of adaptive gain sequences, gradient smoothing and a step rejection procedure to enhance convergence and stability. The algorithm performance is illustrated with the estimation of the most-likely kinetic parameters and initial conditions of a bioprocess model describing the evolution of batch animal cell cultures.

Suggested Citation

  • A. Vande Wouwer & C. Renotte & Ph. Bogaerts, 2006. "A short note on SPSA techniques and their use in nonlinear bioprocess identification," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 12(5), pages 415-422, October.
    • Handle: RePEc:taf:nmcmxx:v:12:y:2006:i:5:p:415-422
    DOI: 10.1080/13873950600723327
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