88 citations to 10.1016/j.neunet.2017.12.007 (Crossref Cited-By Service)
  1. Junfei Qiao, Xin Guo, Wenjing Li, “An online self-organizing modular neural network for nonlinear system modeling”, Applied Soft Computing, 97, 2020, 106777  crossref
  2. Yanjun Zhang, Shancheng Cao, Bintuan Wang, Zhiping Yin, “A Flight Parameter-Based Aircraft Structural Load Monitoring Method Using a Genetic Algorithm Enhanced Extreme Learning Machine”, Applied Sciences, 13, № 6, 2023, 4018  crossref
  3. Nawazish NAVEED, Hayan T. MADHLOOM, Mohd Shahid HUSAIN, “BREAST CANCER DIAGNOSIS USING WRAPPER-BASED FEATURE SELECTION AND ARTIFICIAL NEURAL NETWORK”, acs, 17, № 3, 2021, 19  crossref
  4. Domenico Altieri, Marie-Cécile Robin-Boudaoud, Hannes Kessler, Manuel Pellissetti, Edoardo Patelli, “Machine Learning Approaches for Performance Assessment of Nuclear Fuel Assemblies Subject to Seismic-Induced Impacts”, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 6, № 4, 2020, 041002  crossref
  5. Muhammad Ibnu Choldun Rachmatullah, Judhi Santoso, Kridanto Surendro, “A Novel Approach in Determining Neural Networks Architecture to Classify Data With Large Number of Attributes”, IEEE Access, 8, 2020, 204728  crossref
  6. Marco Cantarini, Danilo Costarelli, Gianluca Vinti, “Asymptotic Expansion for Neural Network Operators of the Kantorovich Type and High Order of Approximation”, Mediterr. J. Math., 18, № 2, 2021, 66  crossref
  7. Eduardo Paluzo-Hidalgo, Rocio Gonzalez-Diaz, Miguel A. Gutiérrez-Naranjo, “Two-hidden-layer feed-forward networks are universal approximators: A constructive approach”, Neural Networks, 131, 2020, 29  crossref
  8. Uğur Kadak, “Fractional type multivariate neural network operators”, Math Methods in App Sciences, 46, № 3, 2023, 3045  crossref
  9. Namig J. Guliyev, Vugar E. Ismailov, “Approximation capability of two hidden layer feedforward neural networks with fixed weights”, Neurocomputing, 316, 2018, 262  crossref
  10. Meejoung Kim, “The generalized extreme learning machines: Tuning hyperparameters and limiting approach for the Moore–Penrose generalized inverse”, Neural Networks, 144, 2021, 591  crossref
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