WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8653

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Author: search.filters.author.Golmankhaneh, Alireza KhaliliScopus Q: search.filters.scopusQuartile.Q1

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  • Article
    Citation - WoS: 41
    Citation - Scopus: 46
    On Artificial Neural Networks Approach With New Cost Functions
    (Elsevier Science inc, 2018) Jafarian, Ahmad; Nia, Safa Measoomy; Golmankhaneh, Alireza Khalili; Baleanu, Dumitru; Measoomy Nia, Safa; Khalili Golmankhaneh, Alireza
    In this manuscript, the artificial neural networks approach involving generalized sigmoid function as a cost function, and three-layered feed-forward architecture is considered as an iterative scheme for solving linear fractional order ordinary differential equations. The supervised back-propagation type learning algorithm based on the gradient descent method, is able to approximate this a problem on a given arbitrary interval to any desired degree of accuracy. To be more precise, some test problems are also given with the comparison to the simulation and numerical results given by another usual method. (C) 2018 Elsevier Inc. All rights reserved.
  • Article
    Structure of Magnetic Field Lines
    (Elsevier Science Bv, 2012) Golmankhaneh, Alireza Khalili; Jazayeri, Seyed Masud; Baleanu, Dumitru; Golmankhaneh, Ali Khalili
    In this paper the Hamiltonian structure of magnetic lines is studied in many ways. First it is used vector analysis for defining the Poisson bracket and Casimir variable for this system. Second it is derived Pfaffian equations for magnetic field lines. Third, Lie derivative and derivative of Poisson bracket is used to show structure of this system. Finally, it is shown Nambu structure of the magnetic field lines. (C) 2011 Elsevier B.V. All rights reserved.