Vol. 36 (2026): Volumen 36
Artículos de investigación

Development of a hñähñu–spanish translator using a statistical algorithm for neural networks and vectorized words

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  • José Manuel Cruz Olguín,
  • Salvador Santos Romero,
  • Virgilio López-Morales,
  • Manuel Alejandro Ojeda Misses
José Manuel Cruz Olguín Universidad Autónoma del Estado de Hidalgo
Salvador Santos Romero Universidad Autónoma del Estado de Hidalgo
Virgilio López-Morales Universidad Autónoma del Estado de Hidalgo
Manuel Alejandro Ojeda Misses Tecnólogico Nacional de México

Published 2026-09-30

Copyright (c) 2026 José Manuel Cruz Olguín, Salvador Santos Romero, Virgilio López-Morales, Manuel Alejandro Ojeda Misses

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

Cruz Olguín, J. M., Santos Romero, S., López-Morales, V., & Ojeda Misses, M. A. (2026). Development of a hñähñu–spanish translator using a statistical algorithm for neural networks and vectorized words. Acta Universitaria, 36, 1-20. https://doi.org/10.15174/au.2026.4778

Abstract

This work presents a statistical-probabilistic (EyP) learning algorithm for training artificial neural networks, applied to Hñähñu–Spanish lexical translation. The proposed method uses statistical normalization of the prediction error and the normal cumulative distribution function to generate adaptive parameters for updating the synaptic weights and bias of the neural network. For validation, a neural network was trained using a bilingual corpus composed of words represented by fixed-length numerical vectors. The performance of the proposed algorithm was evaluated using the mean squared error and compared with the Backpropagation and Levenberg–Marquardt algorithms. The results show that the EyP algorithm is capable of learning lexical associations between the two languages while maintaining stable behavior during the training process. Furthermore, the findings suggest that the proposed methodology represents a viable alternative for scenarios with limited data availability and demonstrate its potential for the development of computational tools aimed at the preservation and digital revitalization of indigenous languages.

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