Modified Neural Net for the Boolean Satisfiability Problem

A modified Hopfield Artificial Neural Network is proposed to solve effectively and efficiently Boolean Satisfiability (SAT) NP-hard problems. The proposed Neural Network is compared against other traditional methods employed in this field, such as Greedy SAT and Genetic Algorithms for SAT. The resul...

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Bibliographic Details
Published in2015 International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE) pp. 64 - 69
Main Authors Mejia-Lavalle, Manuel, Jose Ruiz, A., Joaquin Perez, O., Marilu Cervantes, S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2015
Subjects
Algorithm design and analysis
Artificial intelligence
Artificial Neural Network
Artificial neural networks
Genetic algorithms
Hopfield Neural Net
Neurons
Optimization
Satisfiability Problem
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Summary:A modified Hopfield Artificial Neural Network is proposed to solve effectively and efficiently Boolean Satisfiability (SAT) NP-hard problems. The proposed Neural Network is compared against other traditional methods employed in this field, such as Greedy SAT and Genetic Algorithms for SAT. The results show that the proposed network represents a good alternative given their output quality and response time speed.
ISBN:1467383287
9781467383288
DOI:10.1109/ICMEAE.2015.46