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.
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