IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i4p984-d1069210.html
   My bibliography  Save this article

S-Type Random k Satisfiability Logic in Discrete Hopfield Neural Network Using Probability Distribution: Performance Optimization and Analysis

Author

Listed:
  • Suad Abdeen

    (School of Mathematical Sciences, Universiti Sains Malaysia, Penang 11800 USM, Malaysia
    College of Sciences, King Saud University, Riyadh 11451 KSU, Saudi Arabia)

  • Mohd Shareduwan Mohd Kasihmuddin

    (School of Mathematical Sciences, Universiti Sains Malaysia, Penang 11800 USM, Malaysia)

  • Nur Ezlin Zamri

    (School of Distance Education, Universiti Sains Malaysia, Penang 11800 USM, Malaysia)

  • Gaeithry Manoharam

    (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)

  • Nada Alshehri

    (College of Sciences, King Saud University, Riyadh 11451 KSU, Saudi Arabia)

Abstract

Recently, a variety of non-systematic satisfiability studies on Discrete Hopfield Neural Networks have been introduced to overcome a lack of interpretation. Although a flexible structure was established to assist in the generation of a wide range of spatial solutions that converge on global minima, the fundamental problem is that the existing logic completely ignores the probability dataset’s distribution and features, as well as the literal status distribution. Thus, this study considers a new type of non-systematic logic termed S-type Random k Satisfiability, which employs a creative layer of a Discrete Hopfield Neural Network, and which plays a significant role in the identification of the prevailing attribute likelihood of a binomial distribution dataset. The goal of the probability logic phase is to establish the logical structure and assign negative literals based on two given statistical parameters. The performance of the proposed logic structure was investigated using the comparison of a proposed metric to current state-of-the-art logical rules; consequently, was found that the models have a high value in two parameters that efficiently introduce a logical structure in the probability logic phase. Additionally, by implementing a Discrete Hopfield Neural Network, it has been observed that the cost function experiences a reduction. A new form of synaptic weight assessment via statistical methods was applied to investigate the effect of the two proposed parameters in the logic structure. Overall, the investigation demonstrated that controlling the two proposed parameters has a good effect on synaptic weight management and the generation of global minima solutions.

Suggested Citation

  • Suad Abdeen & Mohd Shareduwan Mohd Kasihmuddin & Nur Ezlin Zamri & Gaeithry Manoharam & Mohd. Asyraf Mansor & Nada Alshehri, 2023. "S-Type Random k Satisfiability Logic in Discrete Hopfield Neural Network Using Probability Distribution: Performance Optimization and Analysis," Mathematics, MDPI, vol. 11(4), pages 1-46, February.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:984-:d:1069210
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/4/984/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/4/984/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Siti Syatirah Muhammad Sidik & Nur Ezlin Zamri & Mohd Shareduwan Mohd Kasihmuddin & Habibah A. Wahab & Yueling Guo & Mohd. Asyraf Mansor, 2022. "Non-Systematic Weighted Satisfiability in Discrete Hopfield Neural Network Using Binary Artificial Bee Colony Optimization," Mathematics, MDPI, vol. 10(7), pages 1-27, April.
    2. Mohd Shareduwan Mohd Kasihmuddin & Siti Zulaikha Mohd Jamaludin & Mohd. Asyraf Mansor & Habibah A. Wahab & Siti Maisharah Sheikh Ghadzi, 2022. "Supervised Learning Perspective in Logic Mining," Mathematics, MDPI, vol. 10(6), pages 1-35, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Abdellah Chehri & Francois Rivest, 2023. "Editorial for the Special Issue “Advances in Machine Learning and Mathematical Modeling for Optimization Problems”," Mathematics, MDPI, vol. 11(8), pages 1-5, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ju Chen & Yuan Gao & Mohd Shareduwan Mohd Kasihmuddin & Chengfeng Zheng & Nurul Atiqah Romli & Mohd. Asyraf Mansor & Nur Ezlin Zamri & Chuanbiao When, 2024. "MTS-PRO2SAT: Hybrid Mutation Tabu Search Algorithm in Optimizing Probabilistic 2 Satisfiability in Discrete Hopfield Neural Network," Mathematics, MDPI, vol. 12(5), pages 1-40, February.
    2. Gaeithry Manoharam & Mohd Shareduwan Mohd Kasihmuddin & Siti Noor Farwina Mohamad Anwar Antony & Nurul Atiqah Romli & Nur ‘Afifah Rusdi & Suad Abdeen & Mohd. Asyraf Mansor, 2023. "Log-Linear-Based Logic Mining with Multi-Discrete Hopfield Neural Network," Mathematics, MDPI, vol. 11(9), pages 1-30, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:984-:d:1069210. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.