Comparison of performance and results in optical recognition hand written numbers using radial basis functions and memetic differential system
The problem optical recognition of handwritten numbers has been approached by different methods, obtaining satisfactory results. In this paper, we propose fuzzy systems with memetic genetic algorithms. Results from this methodology are compared with artificial neuronal networks trained using semi-su...
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| Autores principales: | , , , |
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| Formato: | info:eu-repo/semantics/publishedVersion |
| Lenguaje: | spa |
| Publicado: |
Universidad Antonio Nariño
2021
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| Acceso en línea: | http://revistas.uan.edu.co/index.php/ingeuan/article/view/379 http://repositorio.uan.edu.co/handle/123456789/3944 |
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| Sumario: | The problem optical recognition of handwritten numbers has been approached by different methods, obtaining satisfactory results. In this paper, we propose fuzzy systems with memetic genetic algorithms. Results from this methodology are compared with artificial neuronal networks trained using semi-supervised learning and radial base functions (RBF). It is possible to observe that this kind of neuronal networks offer advantages regarding error rates and time-to-results of the recognition system, compared with methods based in fuzzy systems. |
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