Agent-based Modeling in doing Logic Programming in Fuzzy Hopfield Neural Network

Автор: Shehab Abdulhabib Saeed Alzaeemi, Saratha Sathasivam, Muraly Velavan

Журнал: International Journal of Modern Education and Computer Science @ijmecs

Статья в выпуске: 2 vol.13, 2021 года.

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This paper introduces a new approach to enhance performance in performing logic programming in the Hopfield neural network by using agent-based modeling. Hopfield networks have been broadly utilized to solve problems of combinatorial optimization. However, this network yielded a satisfiability problem because the network has grown larger, and it is more complex. Therefore, an improved algorithm has been proposed to enhance the Hopfield network’s capability by using the technique of fuzzy logic to provide more efficient energy relaxation and to avoid the local minimum solutions. Agent-based modeling has been introduced in this paper to conduct computer simulations, which aim at verifying and validating the introduced approach. By applying the technique of fuzzy Hopfield neural network clustering in the system, better quality solutions are produced, and the network is handled better despite the increasing complexity. Also, the solutions converged faster by the system. Accordingly, this technique of the fuzzy Hopfield neural network clustering in the system has produced better-quality solutions.

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Fuzzy Hopfield neural network, logic programming, agent-based modeling

Короткий адрес: https://sciup.org/15017623

IDR: 15017623   |   DOI: 10.5815/ijmecs.2021.02.03

Список литературы Agent-based Modeling in doing Logic Programming in Fuzzy Hopfield Neural Network

  • A. S. A. Garcez, & G. Zaverucha, (1999). The connectionist inductive learning and logic programming system. Applied Intelligence, 11(1), 59-77.
  • S. Sathasivam, S. A. Alzaeemi, & M. Velavan, (2020). Mean-Field Theory in Hopfield Neural Network for Doing 2 Satisfiability Logic Programming. International Journal of Modern Education & Computer Science, 12(4).
  • J. J. Hopfield, & D. W. Tank, (1985). “Neural” computation of decisions in optimization problems. Biological cybernetics, 52(3), 141-152
  • S. Sathasivam, M. Mansor, M. S. M. Kasihmuddin, & H. Abubakar, (2020). Election Algorithm for Random k Satisfiability in the Hopfield Neural Network. Processes, 8(5), 568.
  • H. Abubakar, & S. Sathasivam, (2020, October). Developing random satisfiability logic programming in Hopfield neural network. In AIP Conference Proceedings (Vol. 2266, No. 1, p. 040001). AIP Publishing LLC.
  • W. A. T. W. Abdullah, (1992). Logic programming on a neural network. International journal of intelligent systems, 7(6), 513-519.
  • S. Sathasivam, (2010). Upgrading logic programming in Hopfield network. Sains Malaysiana, 39(1), 115-118.
  • S. Sathasivam, (2012). Applying Fuzzy Logic in Neuro Symbolic Integration. World Applied Sciences Journal 17 (Special Issue of Applied Math), pp. 79-86.
  • J. Wang, X. Liu, J. Bai, & Y. Chen, (2019). A new stability condition for uncertain fuzzy Hopfield neural networks with time-varying delays. International Journal of Control, Automation and Systems, 17(5), 1322-1329.
  • G. Bologna, (2004). Is it worth generating rules from neural network ensembles?. Journal of Applied Logic, 2(3), 325-348.
  • H. Huang, D. W. Ho, & J. Lam, (2005). Stochastic stability analysis of fuzzy Hopfield neural networks with time-varying delays. IEEE Transactions on Circuits and Systems II: Express Briefs, 52(5), 251-255.
  • J. S. Lin, (1999). Fuzzy clustering using a compensated fuzzy Hopfield network. Neural Processing Letters, 10(1), 35-48.
  • L. A. Zadeh, & R. A. Live, (2019). Fuzzy Logic Theory and Applications. https://doi.org/10.1142/10936.
  • K. Singh, (2016). Fuzzy logic based modified adaptive modulation implementation for performance enhancement in ofdm systems. International Journal of Intelligent Systems and Applications(IJISA), 8(5), 49.
  • J. C. Bezdek, (2013). Pattern recognition with fuzzy objective function algorithms. Springer Science & Business Media.
  • S. A. Alzaeemi, & S.Sathasivam, (2018). Hopfield neural network in agent based modeling. MOJ App Bio Biomech, 2(6), 334-341.
  • S. A. Alzaeemi, S. Sathasivam, M. Velavan, M. Mamat. Agent-based Modeling for Activation Function in Enhancement Logic Programming in Hopfield Neural Network. International Journal of Engineering and Advanced Technology (IJEAT), 9 (4), pp. 1872-1879.
  • E. Bonabeau, (2002). Adaptive agents, intelligence, and emergent human organization: capturing complexity through agent-based modeling: methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences USA, 99, 7280-7287.
  • M. S. M. Kasihmuddin, M. A. Mansor, & S. Sathasivam, (2016). Bezier Curves Satisfiability Model in Enhanced Hopfield Network. International Journal of Intelligent Systems & Applications (IJISA), 8(12).
  • M. A. Mansor, & S. Sathasivam, (2016). Accelerating activation function for 3-satisfiability logic programming. International Journal of Intelligent Systems and Applications (IJISA), 8(10), 44.
  • F. Boufera, F. Debbat, N. Monmarché, M. Slimane, & M. F. Khelfi, (2018). Fuzzy inference system optimization by evolutionary approach for mobile robot navigation. International Journal of Intelligent Systems and Applications (IJISA), 10(2), 85.
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