Complexity of XOR/XNOR boolean functions: a model using binary decision diagrams and back propagation neural networks

Autores
Assi, Ali; Beg, Prasad; Beg, Azam; Prasad, V. C.
Año de publicación
2007
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper proposes a model that predicts the complexity of Boolean functions with only XOR/XNOR min-terms using back propagation neural networks (BPNNs) applied to Binary Decision Diagrams (BDDs). The BPNN model (BPNNM) is developed through the training process of experimental data already obtained for XOR/XNOR-based Boolean functions. The outcome of this model is a unique matrix for the complexity estimation over a set of BDDs derived from Boolean expressions with a given number of variables and XOR/XNOR min-terms. The comparison results of the experimental and BPNNM underline the efficiency of this approach, which is capable of providing some useful clues about the complexity of the circuit to be implemented. It also proves the computational capabilities of NNs in providing reliable classification of the complexity of Boolean functions.
Facultad de Informática
Materia
Ciencias Informáticas
boolean expressions
Neural nets
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/9546
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/9546
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Complexity of XOR/XNOR boolean functions: a model using binary decision diagrams and back propagation neural networksAssi, AliBeg, PrasadBeg, AzamPrasad, V. C.Ciencias Informáticasboolean expressionsNeural netsThis paper proposes a model that predicts the complexity of Boolean functions with only XOR/XNOR min-terms using back propagation neural networks (BPNNs) applied to Binary Decision Diagrams (BDDs). The BPNN model (BPNNM) is developed through the training process of experimental data already obtained for XOR/XNOR-based Boolean functions. The outcome of this model is a unique matrix for the complexity estimation over a set of BDDs derived from Boolean expressions with a given number of variables and XOR/XNOR min-terms. The comparison results of the experimental and BPNNM underline the efficiency of this approach, which is capable of providing some useful clues about the complexity of the circuit to be implemented. It also proves the computational capabilities of NNs in providing reliable classification of the complexity of Boolean functions.Facultad de Informática2007-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf141-147http://sedici.unlp.edu.ar/handle/10915/9546enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Apr07-3.pdfinfo:eu-repo/semantics/altIdentifier/issn/1666-6038info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/3.0/Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T16:32:17Zoai:sedici.unlp.edu.ar:10915/9546Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 16:32:17.899SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Complexity of XOR/XNOR boolean functions: a model using binary decision diagrams and back propagation neural networks
title Complexity of XOR/XNOR boolean functions: a model using binary decision diagrams and back propagation neural networks
spellingShingle Complexity of XOR/XNOR boolean functions: a model using binary decision diagrams and back propagation neural networks
Assi, Ali
Ciencias Informáticas
boolean expressions
Neural nets
title_short Complexity of XOR/XNOR boolean functions: a model using binary decision diagrams and back propagation neural networks
title_full Complexity of XOR/XNOR boolean functions: a model using binary decision diagrams and back propagation neural networks
title_fullStr Complexity of XOR/XNOR boolean functions: a model using binary decision diagrams and back propagation neural networks
title_full_unstemmed Complexity of XOR/XNOR boolean functions: a model using binary decision diagrams and back propagation neural networks
title_sort Complexity of XOR/XNOR boolean functions: a model using binary decision diagrams and back propagation neural networks
dc.creator.none.fl_str_mv Assi, Ali
Beg, Prasad
Beg, Azam
Prasad, V. C.
author Assi, Ali
author_facet Assi, Ali
Beg, Prasad
Beg, Azam
Prasad, V. C.
author_role author
author2 Beg, Prasad
Beg, Azam
Prasad, V. C.
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
boolean expressions
Neural nets
topic Ciencias Informáticas
boolean expressions
Neural nets
dc.description.none.fl_txt_mv This paper proposes a model that predicts the complexity of Boolean functions with only XOR/XNOR min-terms using back propagation neural networks (BPNNs) applied to Binary Decision Diagrams (BDDs). The BPNN model (BPNNM) is developed through the training process of experimental data already obtained for XOR/XNOR-based Boolean functions. The outcome of this model is a unique matrix for the complexity estimation over a set of BDDs derived from Boolean expressions with a given number of variables and XOR/XNOR min-terms. The comparison results of the experimental and BPNNM underline the efficiency of this approach, which is capable of providing some useful clues about the complexity of the circuit to be implemented. It also proves the computational capabilities of NNs in providing reliable classification of the complexity of Boolean functions.
Facultad de Informática
description This paper proposes a model that predicts the complexity of Boolean functions with only XOR/XNOR min-terms using back propagation neural networks (BPNNs) applied to Binary Decision Diagrams (BDDs). The BPNN model (BPNNM) is developed through the training process of experimental data already obtained for XOR/XNOR-based Boolean functions. The outcome of this model is a unique matrix for the complexity estimation over a set of BDDs derived from Boolean expressions with a given number of variables and XOR/XNOR min-terms. The comparison results of the experimental and BPNNM underline the efficiency of this approach, which is capable of providing some useful clues about the complexity of the circuit to be implemented. It also proves the computational capabilities of NNs in providing reliable classification of the complexity of Boolean functions.
publishDate 2007
dc.date.none.fl_str_mv 2007-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/9546
url http://sedici.unlp.edu.ar/handle/10915/9546
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Apr07-3.pdf
info:eu-repo/semantics/altIdentifier/issn/1666-6038
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
dc.format.none.fl_str_mv application/pdf
141-147
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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