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Discrete Mutation Hopfield Neural Network in Propositional Satisfiability

Author

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  • 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)

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.

Suggested Citation

  • Mohd Shareduwan Mohd Kasihmuddin & Mohd. Asyraf Mansor & Md Faisal Md Basir & Saratha Sathasivam, 2019. "Discrete Mutation Hopfield Neural Network in Propositional Satisfiability," Mathematics, MDPI, vol. 7(11), pages 1-21, November.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:11:p:1133-:d:288628
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    References listed on IDEAS

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    Cited by:

    1. Avcı, İbrahim & Lort, Hüseyin & Tatlıcıoğlu, Buğce E., 2023. "Numerical investigation and deep learning approach for fractal–fractional order dynamics of Hopfield neural network model," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).

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