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

A. Vande Wouwer, C. Renotte and Ph. Bogaerts

Mathematical and Computer Modelling of Dynamical Systems, 2006, vol. 12, issue 5, 415-422

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.

Date: 2006
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DOI: 10.1080/13873950600723327

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