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Home All issues Volume 11 (2020) Int. J. Simul. Multidisci. Des. Optim., 11 (2020) 16 References
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Issue
Int. J. Simul. Multidisci. Des. Optim.
Volume 11, 2020
Article Number 16
Number of page(s) 13
DOI https://doi.org/10.1051/smdo/2020008
Published online 07 August 2020
  1. M. YahayaPudza, Z. ZainalAbidin, S. Abdul Rashid, F. MdYasin, A.S.M. Noor, M.A. Issa, Sustainable synthesis processes for carbon dots through response surface methodology and artificial neural network, Processes 7, 704 (2019) [CrossRef] [Google Scholar]
  2. S. Guessasma, D. Bassir, Neural network computation for the evaluation of process rendering: application to thermally sprayed coatings, Int. J. Simul. Multidiscipl. Des. Optim. 8, A10 (2017) [CrossRef] [Google Scholar]
  3. J. Hopfield, D. Tank, Neural' computation of decisions in optimization problems, Biol. Cybernet. 52, 141–152 (1985) [Google Scholar]
  4. J. Hertz, R. Krogh, G. Palmer, Introduction to the Theory of Neural Computation (Addison-Wesley, Reading, MA, 1991) [Google Scholar]
  5. U.P. Wen, K.M. Lan, H.S. Shih, A review of Hopfield neural networks for solving mathematical programming problems, Eur. J. Oper. Res. 198, 675–687 (2009) [CrossRef] [Google Scholar]
  6. J.H. Park, Y.S. Kim, I.K. Eom, K.Y. Lee, Economic load dispatch for piecewise quadratic cost function using Hopfield neural network, IEEE Trans. Power Syst. 83, 1030–1038 (1993) [CrossRef] [Google Scholar]
  7. M.Q. Nguyen, P.M. Atkinson, H.G. Lewis, Superresolution mapping using a Hopfield neural network with fused images, IEEE Trans. Geosci. Remote Sens. 44, 736–749 (2006) [CrossRef] [Google Scholar]
  8. C.H. Fung, M.S. Wong, P.W. Chan, Spatio-temporal data fusion for satellite images using Hopfield neural network, Remote Sens. 11, 2077 (2019) [CrossRef] [Google Scholar]
  9. T.L. Duong, P.D. Nguyen, V.D. Phan, D.N. Vo, T.T. Nguyen, Optimal load dispatch in competitive electricity market by using different models of hopfield lagrange network, Energies 12, 2932 (2019) [CrossRef] [Google Scholar]
  10. A. Kzar, M. Jafri, K. Mutter, S. Syahreza, A modified Hopfield neural network algorithm (MHNNA) using ALOS image for water quality mapping, Int. J. Environ. Res. Public Health 13, 1–92 (2016) [Google Scholar]
  11. S.A. Cook, The complexity of theorem-proving procedures, in Proceedings of the third annual ACM symposium on Theory of computing , 1971, 151–158 [CrossRef] [Google Scholar]
  12. J. Marques-Silva, Practical applications of boolean satisfiability, in 2008 9th International Workshop on Discrete Event Systems , 2008, 74–80 [CrossRef] [Google Scholar]
  13. C. Barrett, C. Tinelli, Satisfiability modulo theories, in Handbook of Model Checking (Springer, Cham, 2018), pp. 305–343 [CrossRef] [Google Scholar]
  14. Y. Shoukry, P. Nuzzo, A.L. Sangiovanni-Vincentelli, S.A. Seshia, G.J. Pappas, P. Tabuada, SMC: satisfiability modulo convex optimization, in Proceedings of the 20th International Conference on Hybrid Systems: Computation and Control , 2017, 19–28 [Google Scholar]
  15. X. Sun, H. Khedr, Y. Shoukry, Formal verification of neural network controlled autonomous systems, in Proceedings of the 22nd ACM International Conference on Hybrid Systems: Computation and Control 2019, 147–156 [Google Scholar]
  16. F. Capelli, Knowledge compilation languages as proof systems, in International Conference on Theory and Applications of Satisfiability Testing (Springer, Cham, 2019), pp. 90–99 [Google Scholar]
  17. J. Li, S. Zhu, G. Pu, L. Zhang, M. Vardi, YSAT-based explicit LTL reasoning and its application to satisfiability checking, Formal Methods Syst. Des. 54, 164–190 (2019) [CrossRef] [Google Scholar]
  18. S. Salcedo-Sanz, R.R. Santiago-Mozos, C. Bousono-Calzon, A hybrid Hopfield network-simulated annealing approach for frequency assignment in satellite communications systems, IEEE Trans. Syst. Man Cybern. B 34, 1108–1116 (2004) [CrossRef] [Google Scholar]
  19. R.A. Kowalski, The Logic for Problem Solving (Elsevier Science Publishing, New York, 1979) [Google Scholar]
  20. G. Pinkas, Symmetric neural networks and propositional logic SAT, Neural Comput. 3, 282–291 (1991) [CrossRef] [Google Scholar]
  21. A.T.W. Wan Abdullah, Logic programming on a neural network, Int. J. Intell. Syst. 7, 513–519 (1992) [CrossRef] [Google Scholar]
  22. S. Sathasivam, Upgrading logic programming in Hopfield nets, Sains Malays. 39, 115–118 (2010) [Google Scholar]
  23. S. Sathasivam, Boltzmann machine and new activation function comparison, Appl. Math. Sci. 78, 3853–3860 (2011) [Google Scholar]
  24. N. Hamadneh, S. Sathasivam, S.L. Tilahun, O.H. Choon, Learning logic programming in radial basis function network via genetic algorithm, J. Appl. Sci. 12, 840–847 (2012) [CrossRef] [Google Scholar]
  25. M. Velavan, Z.R. Yahya, M.N. Abdul Halif, S. Sathasivam, Mean-field theory in doing logic programming using a Hopfield network, Mod. Appl. Sci. 10, 154–160 (2016) [CrossRef] [Google Scholar]
  26. S. Alzaeemi, M.A. Mansor, M.S.M. Kasihmuddin, S. Sathasivam, M. Mamat, Radial basis function neural network for 2 satisfiability programming, Indonesian J. Electr. Eng. Comput. Sci. 18, 459–469 (2020) [CrossRef] [Google Scholar]
  27. S.A.S. Alzaeemi, S. Sathasivam, Hopfield neural network in agent based modeling, MOJ Appl. Biol. Biomech. 2, 334–341 (2018) [Google Scholar]
  28. H. Emami, F. Derakhshan, Election algorithm: a new socio-politically inspired strategy, AI Commun. 28, 591–603 (2015) [CrossRef] [Google Scholar]
  29. M. Kumar, A.J. Kulkarni, Socio-inspired optimization metaheuristics: a review, in Socio-cultural Inspired Metaheuristics (Springer, Singapore, 2019), pp. 241–265 [CrossRef] [Google Scholar]
  30. G. Gosti, V. Folli, M. Leonetti, G. Ruocco, Beyond the maximum storage capacity limit in Hopfield recurrent neural networks, Entropy 21, 726 (2019) [CrossRef] [Google Scholar]
  31. G. Gosti, V. Folli, M. Leonetti, G. Ruocco, Beyond the maximum storage capacity limit in Hopfield recurrent neural networks, Entropy 21, 726 (2019) [CrossRef] [Google Scholar]
  32. A. Barra, M. Beccaria, A. Fachechi, A new mechanical approach to handle generalized Hopfield neural networks, Neural Netw. 106, 205–222 (2018) [CrossRef] [Google Scholar]
  33. W. Gerstner, W.M. Kistler, Mathematical formulations of Hebbian learning, Biol. Cybern. 87, 404–415 (2002) [CrossRef] [Google Scholar]
  34. S. Sathasivam, M. Mansor, M.S.M. Kasihmuddin, H. Abubakar, Election algorithm for random k satisfiability in the Hopfield neural network, Processes 8, 568 (2020) [CrossRef] [Google Scholar]
  35. W. Fernandez de la Vega, Random 2-SAT: results and problems, Theor. Comput. Sci. 265, 131–146 (2001) [CrossRef] [Google Scholar]
  36. D. Du, J. Gu, P.M. Pardalos, Satisfiability Problem: Theory and Applications (American Mathematical Society, 1997), p. 35 [Google Scholar]
  37. K. Vigneshwer, M.A. Mansor, M.S.M. Kasihmuddin, S. Sathasivam, Hybrid imperialistic competitive algorithm incorporated with Hopfield neural network for robust 3 satisfiability logic programming, IAES Int. J. Artif. Intell. 8, 144–155 (2019) [Google Scholar]
  38. M. Peng, N.K. Gupta, A.F. Armitage, An investigation into the improvement of local minima of the Hopfield Network, Neural Netw. 90, 207–212 (1996) [Google Scholar]

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