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Rule-Based Generation of de Bruijn Sequences: Memory and Learning
Cita
Muñoz Ortega, Francisco Javier 
ORCID: https://orcid.org/0000-0002-0874-6275 and Sanz Nuño, Juan Carlos ORCID: https://orcid.org/0000-0003-4708-980X
(2025).
Rule-Based Generation of de Bruijn Sequences: Memory and Learning.
"Mathematics", v. 13
(n. 16);
p. 2598.
ISSN 22277390.
https://doi.org/10.3390/math13162598.
ORCID: https://orcid.org/0000-0002-0874-6275 and Sanz Nuño, Juan Carlos ORCID: https://orcid.org/0000-0003-4708-980X
(2025).
Rule-Based Generation of de Bruijn Sequences: Memory and Learning.
"Mathematics", v. 13
(n. 16);
p. 2598.
ISSN 22277390.
https://doi.org/10.3390/math13162598.
Descripción
| Título: | Rule-Based Generation of de Bruijn Sequences: Memory and Learning |
|---|---|
| Autor/es: |
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| Tipo de Documento: | Artículo |
| Título de Revista/Publicación: | Mathematics |
| Fecha: | 14 Agosto 2025 |
| ISSN: | 22277390 |
| Volumen: | 13 |
| Número: | 16 |
| Materias: | |
| Palabras Clave Informales: | Cellular automata with memory; de Bruijn sequences; neural networ; neural network; sequence generation; Shift Registers |
| Escuela: | E.T.S.I. Montes, Forestal y del Medio Natural (UPM) |
| Departamento: | Matemática Aplicada |
| Licencias Creative Commons: | Reconocimiento |
Resumen
We investigate binary sequences generated by non-Markovian rules with memory length mu, similar to those adopted in elementary cellular automata. This generation procedure is equivalent to a shift register, and certain rules produce sequences with maximal periods, known as de Bruijn sequences. We introduce a novel methodology for generating de Bruijn sequences that combines (i) a set of derived properties that significantly reduce the space of feasible generating rules and (ii) a neural-network-based classifier that identifies which rules produce de Bruijn sequences. The experiments for some values of mu demonstrate the approach's effectiveness and computational efficiency.
Más información
| ID de Registro: | 95345 |
|---|---|
| Identificador DC: | https://oa.upm.es/95345/ |
| Identificador OAI: | oai:oa.upm.es:95345 |
| URL Portal Científico: | https://portalcientifico.upm.es/es/ipublic/item/10384302 |
| Identificador DOI: | 10.3390/math13162598 |
| URL Oficial: | https://www.mdpi.com/2227-7390/13/16/2598 |
| Depositado por: | iMarina Portal Científico |
| Depositado el: | 10 Abr 2026 08:04 |
| Ultima Modificación: | 10 Abr 2026 08:04 |
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