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

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...

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Detalles Bibliográficos
Autores principales: Assi, Ali, Beg, Prasad, Beg, Azam, Prasad, V. C.
Formato: Articulo
Lenguaje:Inglés
Publicado: 2007
Materias:
Ciencias Informáticas
boolean expressions
Neural nets
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/9546
http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Apr07-3.pdf
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-9546
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
boolean expressions
Neural nets
spellingShingle Ciencias Informáticas
boolean expressions
Neural nets
Assi, Ali
Beg, Prasad
Beg, Azam
Prasad, V. C.
Complexity of XOR/XNOR boolean functions: a model using binary decision diagrams and back propagation neural networks
topic_facet Ciencias Informáticas
boolean expressions
Neural nets
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.
format Articulo
Articulo
author Assi, Ali
Beg, Prasad
Beg, Azam
Prasad, V. C.
author_facet Assi, Ali
Beg, Prasad
Beg, Azam
Prasad, V. C.
author_sort Assi, Ali
title Complexity of XOR/XNOR boolean functions: a model using binary decision diagrams and back propagation neural networks
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
publishDate 2007
url http://sedici.unlp.edu.ar/handle/10915/9546
http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Apr07-3.pdf
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