Discrete Mutation Hopfield Neural Network in Propositional Satisfiability

Mohd Shareduwan Mohd Kasihmuddin, Mohd. Asyraf Mansor, Md Faisal Md Basir and Saratha Sathasivam
Additional contact information
Mohd Shareduwan Mohd Kasihmuddin: School of Mathematical Sciences, Universiti Sains Malaysia, Penang 11800 USM, Malaysia
Mohd. Asyraf Mansor: School of Distance Education, Universiti Sains Malaysia, Penang 11800 USM, Malaysia
Md Faisal Md Basir: Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, Johor Bahru 81310 UTM, Johor, Malaysia
Saratha Sathasivam: School of Mathematical Sciences, Universiti Sains Malaysia, Penang 11800 USM, Malaysia

Mathematics, 2019, vol. 7, issue 11, 1-21

Abstract: The dynamic behaviours of an artificial neural network (ANN) system are strongly dependent on its network structure. Thus, the output of ANNs has long suffered from a lack of interpretability and variation. This has severely limited the practical usability of the logical rule in the ANN. The work presents an integrated representation of k -satisfiability ( k SAT) in a mutation hopfield neural network (MHNN). Neuron states of the hopfield neural network converge to minimum energy, but the solution produced is confined to the limited number of solution spaces. The MHNN is incorporated with the global search capability of the estimation of distribution algorithms (EDAs), which typically explore various solution spaces. The main purpose is to estimate other possible neuron states that lead to global minimum energy through available output measurements. Furthermore, it is shown that the MHNN can retrieve various neuron states with the lowest minimum energy. Subsequent simulations performed on the MHNN reveal that the approach yields a result that surpasses the conventional hybrid HNN. Furthermore, this study provides a new paradigm in the field of neural networks by overcoming the overfitting issue.

Keywords: Mutation Hopfield Neural Network; Hopfield neural network; k -satisfiability (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/7/11/1133/pdf (application/pdf)
https://www.mdpi.com/2227-7390/7/11/1133/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:7:y:2019:i:11:p:1133-:d:288628

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().