Fractional Artificial Neural Networks for Growth Models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914166003990528 |
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| author | Najera-Tinoco, Juan Carlos Arciga-Alejandre, Martin P. Sanchez-Ortiz, Jorge Ariza-Hernandez, Francisco J. |
| author_facet | Najera-Tinoco, Juan Carlos Arciga-Alejandre, Martin P. Sanchez-Ortiz, Jorge Ariza-Hernandez, Francisco J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16676 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fractional Artificial Neural Networks for Growth Models Najera-Tinoco, Juan Carlos Arciga-Alejandre, Martin P. Sanchez-Ortiz, Jorge Ariza-Hernandez, Francisco J. Neural and Evolutionary Computing 68T07 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. |
| title | Fractional Artificial Neural Networks for Growth Models |
| topic | Neural and Evolutionary Computing 68T07 |
| url | https://arxiv.org/abs/2511.16676 |