{"ID":2844716,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.16676","arxiv_id":"2511.16676","title":"Fractional Artificial Neural Networks for Growth Models","abstract":"In this paper we present a method to solve initial value problems for fractional growth models, such as generalizations of the exponential and logistic with periodic harvesting models. Using a discretization of the Caputo derivative we propose a fractional artificial neural network, which is implemented in the statistical software R. Moreover, we show examples where the analytical solutions and the approximation of the artificial neural network are compared.","short_abstract":"In this paper we present a method to solve initial value problems for fractional growth models, such as generalizations of the exponential and logistic with periodic harvesting models. Using a discretization of the Caputo derivative we propose a fractional artificial neural network, which is implemented in the statisti...","url_abs":"https://arxiv.org/abs/2511.16676","url_pdf":"https://arxiv.org/pdf/2511.16676v1","authors":"[\"Juan Carlos Najera-Tinoco\",\"Martin P. Arciga-Alejandre\",\"Jorge Sanchez-Ortiz\",\"Francisco J. Ariza-Hernandez\"]","published":"2025-11-08T20:45:25Z","proceeding":"cs.NE","tasks":"[\"cs.NE\"]","methods":"[]","has_code":false}