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Generic Model of Max Heteroassociative Memory Robust to Acquisition Noise

Author

Listed:
  • Valentín Trujillo-Mora

    (Ingeniería en Computación, Universidad Autónoma del Estado de México, Zumpango 55600, Mexico)

  • Marco Moreno-Ibarra

    (Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico City 07700, Mexico)

  • Francisco Marroquín-Gutiérrez

    (Ingeniería Biomédica, Universidad Politécnica de Pachuca (UPP), Zempoala 43830, Mexico)

  • Julio-César Salgado-Ramírez

    (Ingeniería Biomédica, Universidad Politécnica de Pachuca (UPP), Zempoala 43830, Mexico)

Abstract

Associative memories are a significant topic in pattern recognition, and therefore, throughout history, numerous memory models have been designed due to their usefulness. One such model is the associative memory minmax, which is highly efficient at learning and recalling patterns as well as being tolerant of high levels of additive and subtractive noise. However, it is not efficient when it comes to mixed noise. To solve this issue in the associative memory minmax, we present the generic model of heteroassociative memory max robust to acquisition noise (mixed noise). This solution is based on understanding the behavior of acquisition noise and mapping the location of noise in binary images and gray-scale through a distance transform. By controlling the location of the noise, the associative memories minmax become highly efficient. Furthermore, our proposed model allows patterns to contain mixed noise while still being able to recall the learned patterns completely. Our results show that the proposed model outperforms a model that has already solved this type of problem and has proven to overcome existing methods that show some solution to mixed noise. Additionally, we demonstrate that our model is applicable to all associative minmax memories with excellent results.

Suggested Citation

  • Valentín Trujillo-Mora & Marco Moreno-Ibarra & Francisco Marroquín-Gutiérrez & Julio-César Salgado-Ramírez, 2023. "Generic Model of Max Heteroassociative Memory Robust to Acquisition Noise," Mathematics, MDPI, vol. 11(9), pages 1-27, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:9:p:2015-:d:1131364
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    References listed on IDEAS

    as
    1. Julio César Salgado-Ramírez & Jean Marie Vianney Kinani & Eduardo Antonio Cendejas-Castro & Alberto Jorge Rosales-Silva & Eduardo Ramos-Díaz & Juan Luis Díaz-de-Léon-Santiago, 2022. "New Model of Heteroasociative Min Memory Robust to Acquisition Noise," Mathematics, MDPI, vol. 10(1), pages 1-35, January.
    2. Heusel, Judith & Löwe, Matthias & Vermet, Franck, 2015. "On the capacity of an associative memory model based on neural cliques," Statistics & Probability Letters, Elsevier, vol. 106(C), pages 256-261.
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