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

Autor:	C. Renotte, A. Vande Wouwer, Philippe Bogaerts
Rok vydání:	2006
Předmět:	Mathematical optimization Artificial neural network Applied Mathematics Stability (learning theory) Stochastic approximation Computer Science Applications Nonlinear system Simultaneous perturbation stochastic approximation Control and Systems Engineering Modeling and Simulation Convergence (routing) Bioprocess Software Smoothing Mathematics
Zdroj:	Mathematical and Computer Modelling of Dynamical Systems. 12:415-422
ISSN:	1744-5051 1387-3954
DOI:	10.1080/13873950600723327
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::72f5daba76f2f1e51019def14fcdcdde https://doi.org/10.1080/13873950600723327 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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