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Comptes Rendus. Mathématique
Dynamic Programming, Statistical Learning
Parameter Identification by Statistical Learning of a Stochastic Dynamical System Modelling a Fishery with price variation
Comptes Rendus. Mathématique, Volume 358 (2020) no. 3, pp. 245-253.

In this short paper we report on an inverse problem for parameter setting of a model used for the modelling of fishing on the West African coast. We compare the solution of this inverse problem by a Neural Network with the more classical algorithms of optimisation and stochastic control. The Neural Network does much better.

Dans cette courte note nous étudions les performances de l’apprentissage statistique par réseau de neurones pour l’identification des paramètres d’un modèle de pêche. L’idée est d’observer la pêche pendant quelques jours et d’en déduire les paramètres du modèle et donc la biomasse de poisson sur le long terme.

Received:
Accepted:
Published online:
DOI: 10.5802/crmath.2
Classification: 93E20
Pierre Auger 1; Olivier Pironneau 2

1 IRD UMI 209, UMMISCO, Sorbonne Université, Bondy, France
2 LJLL, Sorbonne Université, Paris 75252, cedex 5, France
License: CC-BY 4.0
Copyrights: The authors retain unrestricted copyrights and publishing rights
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     journal = {Comptes Rendus. Math\'ematique},
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Pierre Auger; Olivier Pironneau. Parameter Identification by Statistical Learning of a Stochastic Dynamical System Modelling a Fishery with price variation. Comptes Rendus. Mathématique, Volume 358 (2020) no. 3, pp. 245-253. doi : 10.5802/crmath.2. https://comptes-rendus.academie-sciences.fr/mathematique/articles/10.5802/crmath.2/

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