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Monte Carlo observer for a stochastic model of bioreactors

Joannides M., Larramendy-Valverde I., Rossi V.. 2007. In : Skiadas Christos H. (ed.). Recent advances in stochastic modeling and data analysis. Singapour : World Scientific Publishing [SG], p. 605-612. International conference on Applied stochastic models and data analysis. 12, 2007-05-29/2007-06-01, Chania (Grèce).

This paper proposes a (stochastic) Langevin-type formulation to modelize the continuous time evolution of the state of a biological reactor. We adapt the classical technique of asymptotic observer commonly used in the deterministic case, to design a Monte-Carlo procedure for the estimation of an unobserved reactant. We illustrate the relevance of this approach by numerical simulations.
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