0
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      Modeling wax deposition of crude oils using cascade forward and generalized regression neural networks: Application to crude oil production

      , ,
      Geoenergy Science and Engineering
      Elsevier BV

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Related collections

          Most cited references44

          • Record: found
          • Abstract: not found
          • Article: not found

          Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            A general regression neural network.

            A memory-based network that provides estimates of continuous variables and converges to the underlying (linear or nonlinear) regression surface is described. The general regression neural network (GRNN) is a one-pass learning algorithm with a highly parallel structure. It is shown that, even with sparse data in a multidimensional measurement space, the algorithm provides smooth transitions from one observed value to another. The algorithmic form can be used for any regression problem in which an assumption of linearity is not justified.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              On the evaluation of the viscosity of nanofluid systems: Modeling and data assessment

                Bookmark

                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Geoenergy Science and Engineering
                Geoenergy Science and Engineering
                Elsevier BV
                29498910
                May 2023
                May 2023
                : 224
                : 211613
                Article
                10.1016/j.geoen.2023.211613
                61f300dc-d1cc-4a25-8135-b3a5df764495
                © 2023

                https://www.elsevier.com/tdm/userlicense/1.0/

                https://doi.org/10.15223/policy-017

                https://doi.org/10.15223/policy-037

                https://doi.org/10.15223/policy-012

                https://doi.org/10.15223/policy-029

                https://doi.org/10.15223/policy-004

                History

                Comments

                Comment on this article